Find Academic Supervisors

• Artificial Intelligence
• Machine Learning
• Explainable AI
• +5 more

Dr. Guanjin Wang serves as a Senior Lecturer in the School of Information Technology at Murdoch University, conducting cutting-edge research at the intersection of artificial intelligence, machine learning, and interdisciplinary applications. Her work bridges theoretical advancements with real-world implementations in health informatics and precision agriculture. Education: Joint Ph.D. in Artificial Intelligence, The Hong Kong Polytechnic University and University of Technology Sydney (2018) Her research program centers on developing novel methodologies for learning from complex and imperfect data environments while prioritizing model explainability. Key focus areas include transfer learning for low-resource settings, multimodal learning architectures, fuzzy system innovations, and co-designed AI solutions that integrate domain expertise. Current projects emphasize Aboriginal perinatal mental health prediction and agricultural applications like barley genotype-to-phenotype modeling. Analysis of her recent publications reveals a strong trend toward interdisciplinary collaboration, particularly in healthcare AI where explainability is critical for clinical adoption. Her work consistently addresses data heterogeneity challenges through federated learning, multi-view frameworks, and robust fuzzy systems, with significant contributions to imbalanced data classification and spatiotemporal modeling. Scientific Awards: Google Inclusion Research Award Google Academic Research Award Dr. Wang actively supervises honors and master's students while welcoming PhD inquiries in AI/ML domains. Her research program is supported by approximately 2.2 million AUD in competitive funding from diverse sources including: Google Research (multiple awards) Western Australia Department of Health FHRI Fund Australian Medical Research Future Fund (MRFF) Hong Kong Innovation and Technology Fund German DAAD Healthway Murdoch University Vice Chancellor's Grant She contributes to academic leadership as IEEE Western Australia Section Chapter Chair since 2021 and collaborates with Murdoch's Ngangk Yira Institute for Change on community-engaged research projects.

• Architectural Lighting Design
• Daylighting Systems
• Color in Architecture
• +3 more

Professor Barbara Szybinska Matusiak holds a professorship at the Norwegian University of Science and Technology (NTNU) within the Institute of Architecture and Technology under the Faculty of Architecture and Design . Specializing in Light and Color in Architecture , her work bridges scientific research with practical architectural solutions. She has led major projects like NFR-funded 'DagsLys' and 'HOME', and pioneered the Light & Colour Centre at NTNU (established 2017). Education: Completed a dr.ing. (PhD) at NTNU (1998) on daylighting in linear glass barn buildings at high latitudes. Research Focus: Daylight harvesting systems, façade integration, biophilic design, and lighting ergonomics for occupants. Awards: Member of Norwegian Academy of Science and Letters (2017) and Daylight Academy (2019); nominated for Norwegian Lighting Prize (2019). Her research emphasizes quantifiable metrics for daylight quality, including No-Greenery Line and Greenery-View Factor . She actively chairs international committees like CEN's daylight standardization group and contributes to organizations such as CIE and IEA. Teaching focuses on advanced daylighting and color theory in master's programs. She has supervised four PhD candidates and one post-doctoral researcher, and frequently reviews for top journals like LEUKOS and Energy and Buildings . Key innovations include the Dagslys Laboratory (2002) and ROMLAB (2006), full-scale lighting research facilities. Grants: Multiple IEA SHC Task projects, including Subtask A on user-centered lighting solutions. Publications: Over 100 peer-reviewed articles spanning daylight modeling, façade technology, and cross-cultural lighting studies.

Saša V. NikolićView profile

Professor

• Digital Filter Design
• Signal Processing
• Electronic Circuit Design
• +2 more

Saša V. Nikolić is a Professor of Electronics at the Faculty of Electronic Engineering, University of Niš. He holds a permanent faculty position with extensive contributions to digital filter design and signal processing research. His educational background includes: Bachelor's Degree in Electronics (1991, University of Niš) Master's Degree in Electronics (1996, University of Niš) PhD in Electronics (2004, University of Niš) Nikolić's research focuses on advanced digital filter design techniques, particularly IIR notch filters, Chebyshev approximation methods, and linear phase systems. His work bridges theoretical circuit design with practical signal processing applications in telecommunications and electronic systems. Recent publications demonstrate expertise in optimizing filter magnitude responses while maintaining phase linearity—a critical challenge in real-time signal processing systems. His publication trends reveal sustained contributions to core electronics journals, with emphasis on non-iterative design methods, multi-notch systems, and thermal characterization of electronic components. Key venues include AEU - International Journal of Electronics and Communications (IF: 2.924) and Circuits, Systems and Signal Processing. Nikolić actively participates in national research projects and has co-authored a monograph of exceptional national significance titled 'On the Direct Design of Recursive Digital Filters' (2012). His textbook 'Signali i Sistemi' (2017) serves as foundational material for electronics students at the University of Niš. He maintains laboratory operations at Room 7 of the Faculty of Electronic Engineering (contact: +381 (18) 529-607) and contributes to curriculum development through departmental committees.

• Computer Graphics
• Image Processing
• Scientific Visualization
• +2 more

Miquel Feixas is an Associate Professor in the Department of Computer Science, Applied Mathematics and Statistics at the University of Girona. He is a key member of the Graphics and Image Laboratory (GILab), where he contributes to cutting-edge research in computer graphics, image processing, and scientific visualization. His research focuses on the application of Information Theory to visual computing domains. This includes modeling visual attention, optimizing rendering pipelines, enhancing image quality assessment, and improving scientific data visualization through entropy and mutual information metrics. The analysis of his recent publications reveals a sustained focus on information-theoretic modeling across computer graphics and image analysis. His work bridges theoretical foundations with practical applications in medical imaging, 3D modeling, and data visualization, demonstrating consistent innovation over more than a decade. Scientific Contributions: Co-author of two books on information theory in graphics and image processing Over 100 peer-reviewed publications in top-tier venues Supervision of six doctoral theses Participation in over 20 publicly funded R+D projects Advising and Research Leadership: Miquel Feixas has directed six PhD theses and is actively involved in research management through GILab. His work is supported by multiple competitive grants, reflecting sustained funding and academic impact. He collaborates extensively within interdisciplinary teams focused on visual computing. Research Group: He is an integral member of the Graphics and Image Laboratory (GILab) at the University of Girona, a research group dedicated to advancing graphics, imaging, and visualization technologies through novel computational methods.

Bodo RosenhahnView profile

Professor

• Computer Vision
• Machine Learning
• Big Data Analysis
• +7 more

Bodo Rosenhahn is a Full Professor at Leibniz University Hannover, heading the Institute for Information Processing since September 2008. His research focuses on automated image interpretation with profound expertise in Computer Vision, Machine Learning, and Big Data Analysis. He has established himself as a leading researcher through extensive contributions to the field and successful industry transfer of his work. Rosenhahn received his Computer Science education at the University of Kiel, earning his Dipl.-Inf. in 1999 and Dr.-Ing. in 2003. His academic journey included a postdoctoral position at the University of Auckland (2003-2005), funded by the German Research Foundation, followed by senior researcher work at the Max-Planck Institute for Informatics in Saarbruecken (2005-2008). His research interests span multiple cutting-edge areas including Computer Vision, Machine Learning, 3D Human Pose Estimation, Motion Capture, Object Tracking, Anomaly Detection, and Reinforcement Learning. His work bridges theoretical foundations with practical applications, particularly in medical imaging, autonomous systems, and industrial quality control. The group he leads has developed innovative approaches for video-based motion capture, semantic scene analysis, and multi-object tracking that have achieved state-of-the-art results in numerous challenges. His most recent publications demonstrate strong trends toward explainable AI systems, uncertainty quantification in vision models, robust multi-model fitting techniques, and the integration of quantum principles with machine learning. These works reflect his commitment to developing both theoretically sound and practically applicable computer vision solutions that address real-world challenges in industry and medicine. DAGM-Prize 2002 Dr.-Ing. Siegfried Werth Prize 2003 DAGM-Main Prize 2005 ERC-Starting Grant 2011 (EUR 1.43 million) CVPR 2017 Multi-Object Tracking Challenge PhysRev-A Editors Suggestion 2023 TÜV-Süd Innovation award 2018 As head coach of the LUH AI competition team, Rosenhahn has mentored numerous students who have achieved success in international competitions. His research has been supported by prestigious grants including the ERC Starting Grant and POC Grant. He has also received the Erskine Fellowship for research at the University of Canterbury. Since 2023, he serves as associate editor for IEEE TPAMI, the highest-ranked journal in computer science. Rosenhahn leads a vibrant research group focused on automated image interpretation with multiple ongoing projects including Multiple People Tracking, Relational Object Tracking, Physics-based modeling, Video-based Motion Capture, and Quantum Learning. His group has developed significant datasets such as the Multimodal Motion Capture Indoor Dataset (MPI08) and Multimodal Motion Capture Dataset (TNT15) that have become valuable resources for the computer vision community. The group maintains strong industry connections, successfully transferring research into practical applications while continuing to push the boundaries of fundamental research in computer vision and machine learning.

Roni SenguptaView profile

Assistant Professor

• Computer Vision
• Computer Graphics
• Computational Photography
• +7 more

Roni Sengupta is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill, where she leads the Spatial & Physical Intelligence (SPIN) Lab. Her academic journey includes a Ph.D. from the University of Maryland (2019), a postdoctoral position at the University of Washington (2019-2022), and an undergraduate degree in Electronics and Tele-Communication Engineering from Jadavpur University in India. Her research lies at the intersection of Computer Vision and Computer Graphics, with four primary themes: Inverse Rendering : Recovering physical scene properties like geometry, material reflectance, and lighting 3D Perception from Endoscopy : Monocular depth estimation and SLAM for medical imaging applications Inverse Physics : Recovering 3D geometry and physical properties from sparse video inputs Generative Facial Editing : Developing personalized, training-free methods for facial attribute manipulation Her work has practical applications in visual content creation, telepresence, AR/VR, robotics, and healthcare, with several technologies adopted by companies including Microsoft. Dr. Sengupta's publication record shows a clear trajectory toward increasingly sophisticated inverse problem solving, with recent work (2023-2025) focusing on neural approaches to inverse rendering, medical imaging applications, and personalized generative models. Her research demonstrates strong interdisciplinary connections between computer vision, graphics, and medical applications. Her scientific recognition includes: NIH NIBIB Trailblazer Award for New and Early Stage Investigators (2024) UNC Junior Faculty Development Award (2024) UNC CS Student Association Excellence in Teaching Award (2023) CVPR Best Student Paper Honorable Mentions (2021) Dr. Sengupta actively mentors a diverse team of researchers, including 5 current PhD students, 3 MS students, and several undergraduates. Her former students have gone on to positions at Google, Kitware, Databricks, and Capitol One. She has secured significant research funding, with recent work supported by NIH and industry partners. Her teaching portfolio includes undergraduate and graduate courses in computer vision, 3D generative models, and neural rendering. The SPIN Lab maintains strong industry connections, with research collaborations and technology adoption by Microsoft, NVIDIA Research, and Snapchat Research. The lab's work on background matting has been particularly influential, receiving recognition at CVPR 2021 and being adopted by multiple companies.

Javier Civera SanchoView profile

Associate Professor

• Computer Vision
• Robotics
• Artificial Intelligence
• +5 more

Javier Civera Sancho is an Associate Professor at the University of Zaragoza, where he serves as Deputy Director of the Institute of Research in Engineering of Aragon (I3A). He is affiliated with the School of Engineering and Architecture in the Department of Computer Science and Systems Engineering, where he leads research in the RoPeRT group (Robotics, Computer Vision and Artificial Intelligence). With a background in Industrial Engineering and a PhD in Systems and Computer Engineering, both from the University of Zaragoza, Civera has established himself as a prominent researcher in computer vision and robotics. Industrial Engineering degree (2004, University of Zaragoza) PhD in Systems and Computer Engineering (2009, University of Zaragoza) Civera's research primarily focuses on computer vision, with special emphasis on SLAM (Simultaneous Localization and Mapping), 3D reconstruction, and spatial artificial intelligence. His work aims to provide computers with human-like visual capabilities, including object recognition, 3D structure estimation, spatial context understanding, and tracking of moving objects. He has maintained a long-standing research line in scene localization and mapping, making significant contributions to visual SLAM methodologies and applications. His publication trends reveal a strong focus on advancing visual SLAM techniques, with recent work exploring neural rendering integration, semantic mapping, robust camera calibration, and applications in challenging environments like agriculture. His research bridges theoretical computer vision with practical robotics applications, showing increasing integration with deep learning approaches while maintaining strong geometric foundations. 2 research sexenios (with last granted for 2012-2017) 2 teaching quinquenios h-index: 21 (Google Scholar) 397 citations in last 5 years (Google Scholar) Civera has directed 2 doctoral theses and is currently supervising 4 PhD students. His research has been supported through various institutional frameworks at the University of Zaragoza, including his role in the I3A which provides critical research infrastructure. He is actively involved in guiding the next generation of researchers in computer vision and robotics, emphasizing both theoretical understanding and practical implementation. As a member of the RoPeRT research group, Civera collaborates with a multidisciplinary team working at the intersection of robotics, computer vision, and artificial intelligence. His work often involves developing systems that integrate multiple sensor modalities and create robust spatial understanding for autonomous agents.

Marcin AdamczykView profile

Assistant Professor

• Mechanical Engineering
• Optics
• 3D Imaging
• +6 more

Marcin Adamczyk is an Assistant Professor at the Institute of Micromechanics and Photonics, Faculty of Mechatronics, Warsaw University of Technology. His research primarily focuses on 3D imaging systems, structured light technologies, and addressing temperature-related challenges in optical measurement systems. With a PhD obtained in 2019, he has established himself as a significant contributor to the field of mechanical engineering with specialization in optical systems. His research interests span across multiple domains of optical engineering and measurement systems: Mechanical Engineering with focus on precision systems Optics and structured light technologies 3D imaging, scanning, and digitization Camera calibration and thermal compensation Motion capture and kinematic analysis Metrology and measurement accuracy Dr. Adamczyk's publication record demonstrates a consistent focus on addressing thermal effects in 3D measurement systems. His recent work (2022-2024) has concentrated on developing methodologies for camera thermal drift compensation, optomechanical modifications for industrial applications, and optimal test stand design. Earlier works (2013-2020) established his expertise in temperature compensation methods for various components of 3D structured light scanners, including raster projectors and mechanical bases. His research has practical applications in crime scene documentation, human body motion analysis, and cultural heritage preservation. His scholarly contributions have earned him an h-index of 9 (Scopus) and 8 (Web of Science), with a total CiteScore of 48.45. His work has been recognized with two specific achievements, though details are not provided in the available information. Dr. Adamczyk has supervised 11 promoted theses, indicating an active role in mentoring the next generation of engineers and researchers. His research has been supported by at least 4 projects, though specific details about funding sources and grant amounts are not provided in the available information. He is part of the research team at the Institute of Micromechanics and Photonics at Warsaw University of Technology, collaborating with researchers such as Robert Sitnik, Maciej Karaszewski, and Marcin Witkowski. His work appears to be centered around the development and refinement of 3D measurement systems for various applications including forensic science, biomechanics, and cultural heritage preservation.

Carlos Fernando Avenancio-LeónView profile

Assistant Professor

• Equitable Finance
• Corporate Finance
• Household Finance
• +7 more

Carlos Fernando Avenancio-León is an Assistant Professor of Finance at the Rady School of Management, University of California San Diego. His academic work focuses on the intersection of finance, political power, and economic inequality, with particular attention to how financial mechanisms affect disadvantaged communities and shape economic outcomes. Professor Avenancio-León's research centers on what he terms 'Equitable Finance'—examining how financial systems and institutions contribute to or mitigate economic inequality. His work spans Corporate Finance, Household Finance, and Labor & Finance, with additional interests in Empirical Legal Studies and Political Economy. He investigates how political empowerment translates to economic advancement, particularly for marginalized communities, and how financial institutions either reinforce or reduce existing inequalities. His research has revealed systematic racial disparities in property tax assessments, demonstrating how Black and Latinx homeowners systematically pay higher effective tax rates due to assessment practices that overvalue properties in minority neighborhoods. This work, published in top journals like the Quarterly Journal of Economics, has been widely covered in major media outlets including The New York Times, The Wall Street Journal, and The Guardian. His research on voting rights shows how political empowerment through the Voting Rights Act of 1965 translated to tangible economic improvements for Black Americans. 2018 Equitable Growth grantee for research on voting rights and economic inequality Professor Avenancio-León collaborates extensively with researchers across multiple institutions and disciplines. His work bridges finance, economics, law, and political science to address systemic inequalities. He serves as a Nonprofit Board Member and Treasurer of Proyecto Matria, a Puerto Rico-based organization fighting gender-based violence that has received support from MacKenzie Scott's Yield Giving initiative. His commitment to addressing economic inequality extends to advocacy for political representation for Puerto Rico, which he views as both a political and economic issue affecting more than 3 million disenfranchised Americans.

• Multimodal Generative AI
• Computer Vision
• Machine Learning
• +3 more

Vicky Kalogeiton is a Professor in AI at École Polytechnique's Computer Science Laboratory (LIX) and leads the VISTA team. As an ELLIS member, her research focuses on multimodal generative AI with applications in medical imaging, efficient generation, and structured output modeling. She actively publishes in top venues like CVPR, ICCV, and IJCV, and supports Slow/ Open Science principles. PhD from University of Edinburgh and INRIA Grenoble Habilitation (HDR) from École Polytechnique 2024 Hi!Paris Chaire and multiple grants (ANR, Microsoft, DIM RFSI) Her recent work explores diffusion models for visual geolocation (Around the World in 80 Timesteps), camera motion control (AKiRa & E.T. dataset), and multimodal humor detection (FunnyNet-W). She pioneered coherence-aware training frameworks and cinematic trajectory analysis methods. Scientific recognition includes CVPR 2024 Highlight paper ACCV 2022 Student Honorable Mention ICCV-W 2021 Best Paper Award Outstanding Reviewer Awards (CVPR, ICCV, ECCV) She supervises current PhD candidates and has mentored numerous students across institutions like MBZUAI, Inria, and Telecom Paris. Her teaching includes Advanced Deep Learning and Computer Vision courses at École Polytechnique.

Sanja FidlerView profile

Associate Professor

• Computer Vision
• Machine Learning
• 3D Reconstruction
• +5 more

Sanja Fidler is an Associate Professor at the University of Toronto (Department of Computer Science) within the Mathematical and Computational Sciences School. She is also a co-founder and affiliated faculty at the Vector Institute and serves as VP of AI Research at NVIDIA. Her research focuses on the intersection of Computer Vision, Machine Learning, and Graphics, with emphasis on 3D reconstruction, generative models, and interactive methods. She previously held a Research Assistant Professor position at the Toyota Technological Institute at Chicago. Research Interests: Dr. Fidler specializes in Computer Vision and Machine Learning, particularly in areas such as 3D vision, 3D reconstruction/synthesis, object detection, and interactive scene understanding. Her work bridges graphics and vision, addressing challenges in geometric representations, generative models, and real-world applications like medical imaging and autonomous systems. Awards: She has received notable awards including the Canada CIFAR AI Chair (2018), Connaught Innovation Award (2020), NVIDIA Pioneer of AI Award (2016), and multiple outstanding reviewer distinctions at top conferences. Her teaching excellence was recognized with the Professor of the Year award by UofT's CS Student Union in 2014/2015. Collaborations & Labs: Leading the NVIDIA Toronto AI Research Lab and contributing to initiatives at the Vector Institute, her work integrates academic and industrial research. She co-organized workshops on topics like 'Geometry Meets Deep Learning' and '3D Indoor Scene Understanding' at major conferences.

Peter de WithView profile

Professor

• Video Compression
• Image Analysis
• Healthcare Applications
• +4 more

Peter de With is a Part-time Full Professor in the Department of Electrical Engineering at Eindhoven University of Technology (TU/e), leading the Video Coding & Architectures Group. His expertise spans video compression, image analysis in healthcare, surveillance, and multimedia. He holds a PhD from Delft University of Technology (1992) and has held roles at Philips Research, the University of Mannheim, and Cyclomedia Technology. His research focuses on video/image signal processing, machine learning, and healthcare applications such as esophageal cancer detection. He is an IEEE Fellow and has authored over 400 papers, receiving multiple awards for his work. Research interests include video coding, medical imaging, and system innovations in health, surveillance, and automotive sectors. Key publications address challenges in medical instrument detection, 3D ultrasound, and multiview rendering. He collaborates with industry partners like Bosch Security and hospitals for oncology research. His work bridges academic innovation with real-world applications, emphasizing practical solutions in healthcare technology and multimedia systems. Awards include IEEE Fellow status and paper awards from CE Chester Sall, SPIE, and Elsevier. He chairs program committees for IEEE conferences and holds 30 patents. His contributions to video analysis and system design have driven advancements in safety, security, and healthcare technologies.

• Generative Models
• Machine Reasoning
• AI Safety
• +3 more

Dr. Chris Willcocks is an Associate Professor in the Department of Computer Science at Durham University, specializing in generative models and machine reasoning. He has authored over 35 peer-reviewed publications in top-tier venues including ICLR, TPAMI, CVPR, and IEEE TIFS. His research group has made significant contributions to theoretical generative modeling, including ∞-Diff for infinite-resolution synthesis and anomaly detection techniques like AnoDDPM. His research interests focus on theoretical generative modeling, machine reasoning frameworks, and AI safety. Key projects include diffusion models in Hilbert spaces, gradient origin networks, and applications in medical anomaly detection and security threat analysis. Teaching responsibilities include deep learning, reinforcement learning, and cybersecurity modules. Industry collaborations span multinational corporations (P&G, Unilever, Dyson) and public sector organizations (NHS, DSTL), with over 15 invited talks on AI ethics and cybersecurity. Awards include fellowship of the Higher Education Academy. Professional activities include serving as area chair for BMVC and admissions tutor for computer science. Willcocks supervises multiple postgraduate students including Jonathan Frawley and Rita Liu, and maintains a YouTube channel with educational content on deep learning.

Eric GrimsonView profile

Professor

• Computer Vision
• Medical Image Analysis
• Artificial Intelligence
• +3 more

Eric Grimson is Chancellor for Academic Advancement at MIT, serving as a central advisor to the president on institutional campaigns and representing MIT to alumni and parents. He is also a Professor of Computer Science and Bernard M. Gordon Professor of Medical Engineering, overseeing MIT’s online education initiatives as Interim Vice President for Open Learning. His research focuses on computer vision and medical image analysis, with contributions to algorithms for medical imagery and open-access platforms like 3D Slicer. Grimson has taught over 15,000 undergraduates and advised nearly 50 PhD students. He holds honorary doctorates from Dalhousie University and the University of Saskatchewan/Regina. Education: BSc in Mathematics & Physics (University of Regina), PhD in Mathematics (MIT). Professional roles include former EECS Department Head and Acting MIT Chancellor. Research affiliations include the Medical Vision Group and Vision Group, advancing AI in healthcare and computer vision. Research Interests : Computer vision applications in medical imaging Medical engineering and neuroimaging Online education platforms Unsupervised learning and Bayesian models Awards : AAAI Fellow (2000) ACM Fellow IEEE Fellow Multiple honorary doctorates Grimson’s work bridges academia and innovation, leading MIT’s Open Learning initiatives while maintaining a robust research agenda in vision and medical computing.

Talmo PereiraView profile

Research Fellow

• Artificial Intelligence
• Deep Learning
• Computer Vision
• +4 more

Talmo Pereira, PhD, is a Salk Fellow and Principal Investigator leading a research lab at the Salk Institute for Biological Studies. His work focuses on developing computational tools using deep learning and computer vision to study biological motion across species, including animal behavior, plant root systems, and neural coordination mechanisms. Pereira pioneered markerless motion capture technology and created the widely adopted SLEAP software for behavioral analysis. Education: BS in Bioinformatics and Computational Biology from University of Maryland, Baltimore County MS in Neuroscience from Princeton University PhD in Neuroscience from Princeton University Research Focus: Pereira's lab bridges artificial intelligence and biological systems, developing methods to quantify movement patterns in health and disease states. Key innovations include deep learning frameworks for behavioral phenotyping, neural circuit analysis, and plant root dynamics. The research emphasizes open-source tool development for global scientific collaboration. Publications: Pereira's recent articles demonstrate a consistent focus on AI-driven analysis of biological systems, with strong themes in behavioral quantification (animal and human), neural circuitry, plant phenomics, and biomedical applications. His work frequently involves interdisciplinary collaboration and open-source tool development. Awards & Honors: Society for Social Neuroscience Open Science Award (2022) Harold M. Weintraub Graduate Student Award (2022) Princeton Porter Ogden Jacobus Fellowship (2020) NSF Graduate Research Fellowship (2015) NIH Undergraduate Training Award (2013) Multiple undergraduate honors including HHMI Scholar and Meyerhoff Scholar Team & Advising: Leads a diverse team including postdoctoral researchers, PhD students, bioinformatics analysts, and undergraduate interns. Current advisees include PhD student Scott Yang and Master's students Shrivaths Shyam and Aaditya Prasad. The lab has trained over 20 alumni who progressed to top academic and industry positions.