Dr. Yang Zhang is a Professor at the National University of Singapore (NUS), holding appointments in the Department of Computer Science (School of Computing) and the Department of Biochemistry (Yong Loo Lin School of Medicine). He also leads the Zhang Lab, which focuses on AI-driven computational methods for protein structure prediction and design. Previously, he was a Professor at the University of Michigan. His research integrates artificial intelligence, deep learning, and physics-based models to address challenges in computational biology. Affiliations: School of Computing; Yong Loo Lin School of Medicine; Cancer Science Institute of Singapore Key Roles: Principal Investigator of Zhang Lab; Developer of I-TASSER algorithm Research interests span AI-driven protein design, deep learning for RNA structure prediction, and drug discovery. Projects include the EvoDesign server for protein interaction design and TripletRes for coevolution-based contact prediction. Major contributions include the I-TASSER algorithm, ranked top in CASP experiments for protein structure prediction. Awards: Alfred P. Sloan Award, NSF CAREER Award, and seven-time Highly Cited Researcher (2015–2021).
Jeong Joon (JJ) Park is an Assistant Professor in the Department of Computer Science and Engineering at the University of Michigan. His research focuses on computer vision, graphics, and artificial intelligence with applications in 3D/4D reconstruction, generative modeling, robotics, and medical imaging. He holds a position in the College of Engineering and actively seeks PhD students and postdoctoral researchers aligned with his research interests. Dr. Park’s work emphasizes interdisciplinary approaches, combining geometric deep learning with generative models to address challenges in scene understanding, novel view synthesis, and multi-modal perception. His lab explores both foundational techniques and applied systems, often collaborating with industry and academia on real-world problems. Key research directions include diffusion models for sparse data restoration, trajectory-conditioned 4D generation, and uncertainty-aware sensor fusion for autonomous systems. His publications span top-tier conferences like CVPR, ICCV, and NeurIPS, reflecting a strong publication record in computer vision and graphics. He teaches courses in computer vision and advises students on advanced projects requiring significant weekly commitments. Prospective applicants are encouraged to apply through the U-M CSE PhD program and contact him directly for collaboration opportunities.
Michael Schaub is a tenure-track Assistant Professor in the Department of Computer Science at RWTH Aachen University, specializing in Computational Network Science. His research focuses on analyzing complex systems through network and graph models, integrating dynamical systems, control theory, and machine learning. He leads the Computational Network Science group, advancing methodologies for higher-order network models like simplicial complexes and hypergraphs. Schaub holds a PhD from Imperial College London and has held postdoctoral positions at MIT and Oxford. He is an ERC Starting Grant recipient (2022) and a Marie Curie Fellow, recognized for contributions to network dynamics and topological data analysis. Education: PhD in Mathematics, Imperial College London (2011-2015) MSc in Biomedical Engineering, Imperial College London (2010) BSc in Electrical Engineering, ETH Zurich (2007-2010) Research Interests: Schaub’s work spans interdisciplinary applications of network science, including biological systems, social networks, and technical infrastructures. Key areas include: Higher-order network models (hypergraphs, simplicial complexes) Graph signal processing and dynamics on networks Community detection and dynamical systems analysis Topological data analysis and machine learning Grants & Awards: ERC Starting Grant (2022): HIGH-HOPeS project Marie Skłodowska-Curie Fellowship (2017-2019) Junior Fellow, German Informatics Society (GI) Member of Junges Kolleg (North Rhine-Westphalia Academy) Labs & Teams: Leads the Computational Network Science Lab at RWTH Aachen, collaborating internationally on projects like the ELLIS Society and the European Laboratory for Learning and Intelligent Systems (ELLIS). Active in organizing workshops (e.g., Toponets, SIAM MDS).
WonSook Lee is a tenured Full Professor in the School of Electrical Engineering and Computer Science at the University of Ottawa’s Faculty of Engineering. Her expertise spans medical imaging, machine/deep learning, computer graphics, and computer vision. She earned her Ph.D. in Computer Science from the University of Geneva (Switzerland) and holds degrees from POSTECH (Korea) and NUS (Singapore). Before academia, she worked at Korea Telecom, Samsung Advanced Institute of Technology, and Eyematic Interfaces Inc. (USA). Her research focuses on applications such as virtual/augmented reality, MRI/CT/Ultrasound analysis, and 3D mesh modeling. She has authored over 130 publications, including 30+ journal papers, and serves on conference committees and editorial boards. Lee has secured major grants (NSERC, CFI, ORF) as Principal Investigator and contributed to global initiatives like South Korea’s National Research Foundation. Her lab explores cutting-edge techniques in medical imaging, AI-driven object detection, and multimodal systems. Notable projects include adversarial perturbation analysis for model robustness, cross-domain GANs for semantic segmentation, and real-time ultrasound-enhanced pronunciation training. She actively promotes interdisciplinary research in healthcare technology and autonomous systems.
Shiva Nejati is a Professor at the University of Ottawa 's School of Electrical Engineering and Computer Science . He holds a PhD in Computer Science from the University of Toronto and previously worked as a Senior Scientist (2012-2019) and Scientist (2009-2012) at the SnT Centre (University of Luxembourg) and Simula Research Laboratory. Research focus: Software engineering for cyber-physical systems (autonomous vehicles, IoT), blending formal verification, machine learning, and search-based testing Key tools developed: ARIsTEO, SOCRaTEs, SimCoTest, EPIcuRus Editorial roles: Associate Editor for EMSE Journal (2025–), ASE Journal (2025–), IEEE Transactions on Software Engineering (2020–2024) His work combines formal methods , empirical software engineering , and AI/ML to address verification challenges in complex systems, particularly through evolutionary algorithms and surrogate modeling . Notable collaborations include industry partners in telecommunications, automotive, and aerospace sectors. Recent publications emphasize large language models for requirements analysis, adversarial testing of vision systems, and multi-objective optimization for test generation. His Sedna Research Lab actively trains graduate students in these cutting-edge methodologies.
Julia Kempe is a Silver Professor of Computer Science, Mathematics, and Data Science at New York University (NYU), holding joint appointments at the Courant Institute and the Center for Data Science (CDS). She serves as Director of the CDS and is on research leave at the CSD, ENS, Paris (2023–24). Her expertise spans interdisciplinary research in quantum computing, machine learning, and data science. She holds PhDs in Mathematics (UC Berkeley, 2001) and Computer Science (École Nationale Supérieure des Télécommunications, Paris, 2001), alongside advanced degrees in theoretical physics and mathematics from prestigious institutions in France and Austria. Research Interests: Data Science, Machine Learning (theoretical foundations and applications to physics), and past contributions to quantum computing. She focuses on robustness in machine learning models, adversarial examples, and interdisciplinary applications of physics-informed AI. Awards and Honors: Knight of the National Order of Merit (France, 2010), Femme en Or de la Recherche (France, 2010), ERC Starting Grant (2007, top-ranked in Europe), and numerous academic fellowships. She is a member of Academia Europaea (2018) and a Fellow of the Asia-Pacific Artificial Intelligence Association (2022). Grants and Leadership: Principal investigator of NSF NRT grants for CDS PhD programs, co-PI on NASA TCAN grants, and leader in NYU’s Senior Leadership Team. She designed NYU’s Data Science undergraduate programs and expanded interdisciplinary collaborations in machine learning and quantum computing. Labs and Teams: Directs the CDS, collaborates with the Courant Institute, and leads research initiatives in Paris. Her work bridges theoretical computer science, physics, and applied data science, emphasizing interdisciplinary innovation.
Michela Becchi is an Associate Professor in the Department of Electrical and Computer Engineering at North Carolina State University. She specializes in computer architecture, systems software, and applications, with a focus on heterogeneous systems, parallel algorithms, and acceleration techniques for bioinformatics, pattern recognition, and quantum computing. Her work spans multi-core CPUs, GPUs, FPGAs, and distributed clusters, emphasizing the boundary between hardware and software design. Dr. Becchi holds a Ph.D. and Master’s degree in Computer Engineering from Washington University in St. Louis (2009) and a Bachelor’s degree in Computer Engineering from Politecnico di Milano, Italy (2000). Her research has been recognized with prestigious awards, including the NSF CAREER Award (2015) and the University of Missouri System President Award for Early Career Excellence (2016). Her research interests include compiler and runtime techniques for heterogeneous systems, acceleration of bioinformatics algorithms, and high-speed networking applications. She has pioneered frameworks for efficient data transformation, GPU-accelerated compression, and memory-efficient graph algorithms for quantum computing. Her work also explores thread coarsening, mixed-precision auto-tuning, and secure multi-core processor design. Key contributions include the PILOT runtime system for GPU memory management, the GPU-FPtuner auto-tuner for floating-point applications, and innovative approaches to automata processors for genomic analysis. Her publications emphasize reproducible accuracy in scientific simulations and the optimization of irregular applications on many-core platforms.
Rodrigo Ventura is an Associate Professor at the Department of Electrical and Computer Engineering, Instituto Superior Técnico (IST), University of Lisbon. He is also a senior researcher at the Institute for Systems and Robotics (ISR-Lisbon), leading the Space and Aeronautics thematic line. His research focuses on the intersection of Robotics and Artificial Intelligence, emphasizing human-robot interaction, space robotics, and cognitive architectures. He coordinates the Minor in Space Sciences and Technologies at IST and the MBE on Space Systems for Tecnico+. As Adjoint Faculty at the International Space University (ISU), he contributes to global academic initiatives. His work includes experiments on the International Space Station and participation in analog space missions. Research interests span biologically inspired systems, machine learning, and teleoperation interfaces. Recent publications address reinforcement learning for UAVs, microgravity experiments, and pseudo-haptic feedback for robotic control. He teaches subjects like Artificial Intelligence and Decision Systems, Satellite Engineering, and Autonomous Systems.
Eung-Joo Lee is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Arizona, where he also holds affiliations with the Department of Ophthalmology and Vision Science, the BIO5 Institute, and the UA Cancer Center. He serves as an adjunct professor at the University of Nebraska–Lincoln and is a member of the Graduate Faculty. Dr. Lee leads the Vision Systems and Intelligence (VSI) Laboratory and contributes to interdisciplinary research bridging engineering and medicine. Education: PhD in Electrical and Computer Engineering, University of Maryland, College Park, 2021 MS in Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea, 2015 BS in Electrical Engineering, University of Texas at Dallas, 2013 Dr. Lee's research centers on developing computationally efficient and interpretable deep learning models for real-time, low-resource environments, particularly in computer vision and medical imaging. His work addresses perception and decision-making challenges in autonomous and medical systems. He applies cross-disciplinary expertise in engineering and medicine to create lightweight AI solutions. Although no specific publications are listed in the provided text, his research direction suggests strong engagement in areas such as embedded AI, medical image analysis, and real-time computer vision systems, likely published in top-tier venues in machine learning and biomedical engineering. Scientific Service and Recognition: Associate Editor, Journal of Signal Processing Systems (Springer) Editorial Board Member, Scientific Reports (Nature Portfolio) Reviewer for IEEE Transactions on Pattern Analysis and Machine Intelligence, Medical Image Analysis, Nature Machine Intelligence, and others Active participant in major conferences including NeurIPS, CVPR, MICCAI, AAAI, and SPIE Dr. Lee advises research through the VSI Laboratory and contributes to academic leadership via service on the Scientific Advisory Committee for the Body and Imaging Center at the University of Arizona. He has served on numerous program committees, organized workshops, and chaired sessions at international conferences, demonstrating growing leadership in the academic community. He is actively involved in interdisciplinary research collaborations, including past work with Children’s National Hospital and the U.S. Army Research Laboratory, and continues to bridge gaps between engineering and clinical applications.
Roxana Geambasu is an Associate Professor at Columbia University's Department of Computer Science, with affiliations to the Software Systems Lab and the Cybersecurity Committee . She specializes in systems security, differential privacy, and resource management for modern computing environments. PhD: University of Washington (2011) Undergraduate: Polytechnic University of Bucharest, Romania Her research focuses on integrating differential privacy as a first-class computing resource in infrastructure systems, with key contributions in: Privacy Budget Management (PrivateKube, DPack, Turbo) Web & Mobile Privacy (Cookie Monster, Big Bird) Security Abstractions (POSIX, Vanish, XRay) Article trends show a strong emphasis on differential privacy (8/15), resource scheduling (5/15), and privacy-preserving advertising (3/15). Key subfields include budget optimization, browser APIs, and ML pipeline privacy. Scientific awards include: Alfred P. Sloan Fellowship NSF CAREER Award Google Ph.D. Fellowship in Cloud Computing Best Paper Awards at SOSP, EuroSys She advises M.S. and undergraduate students, teaching courses in Distributed Systems and Privacy Curriculum . Her lab develops tools like PixelDP and Sunlight for operationalizing privacy in real-world systems.
Karen Livescu is a Professor at the Toyota Technological Institute at Chicago (TTIC), a philanthropically endowed graduate institute for computer science located on the University of Chicago campus. She also serves as a courtesy faculty member in the Department of Computer Science at the University of Chicago and is an Affiliated Scholar at the Data Science Institute there. Her research focuses on advancing speech and language processing through innovative machine learning approaches. Education: PhD in Electrical Engineering and Computer Science from MIT (2005) S.M. from MIT Department of Electrical Engineering and Computer Science (1999) A.B. in Physics from Princeton University (1996) Karen's research spans multiple dimensions of speech and language processing with particular emphasis on speech recognition, spoken language understanding, and multimodal processing. She has made significant contributions to articulatory feature-based speech recognition, self-supervised learning for speech representation, and sign language processing. Her work consistently bridges machine learning techniques with linguistic and speech science knowledge, focusing on creating more robust, interpretable, and inclusive speech processing systems that can handle diverse languages and modalities. Her recent publication trajectory reveals a strong focus on self-supervised learning for speech representation, multilingual speech processing, and sign language understanding. She has been instrumental in developing benchmark frameworks like SUPERB and ML-SUPERB that have become standard evaluation tools in the speech community. Her work increasingly addresses critical challenges in low-resource language scenarios, language disparities in speech technology, and ethical considerations in real-world deployment. Scientific Awards: Best Paper award at EMNLP 2024 for 'Towards robust speech representation learning for thousands of languages' Best Student Paper Award at ASRU 2023 Best Short Paper Award at CRAC 2021 Top system at WMT-SLT 2023 Karen has successfully advised numerous PhD students and postdoctoral researchers who have gone on to faculty positions at institutions like University of Waterloo, University of Edinburgh, and Stellenbosch University, as well as industry roles at major technology companies including Google, Meta, and NVIDIA. Her research group has secured significant funding for projects including the development of the SLUE benchmark for spoken language understanding and the SUPERB framework for evaluating self-supervised speech models. She has been actively involved in organizing workshops and symposia that bring together researchers in speech and language processing. Karen leads the Speech and Language at TTIC (SL@TTIC) research group, which maintains a strong collaborative relationship with researchers at the University of Chicago and other institutions. The group has been particularly active in advancing sign language processing through projects like ChicagoFSWild and OpenASL, while also making significant contributions to spoken language understanding and multilingual speech recognition. Her team regularly participates in community challenges and benchmarks, helping to push the field forward through open science and collaborative evaluation frameworks.
Stephen W. Keckler is an Adjunct Professor at the Department of Computer Science , The University of Texas at Austin , and serves as Vice President of Architecture Research at NVIDIA . He is an ACM Fellow , IEEE Fellow , and Sloan Foundation Research Fellow . Education: BS in Electrical Engineering, Stanford University (1990) SM in Computer Science, Massachusetts Institute of Technology (1992) PhD in Computer Science, MIT (1998) Research Interests focus on computer architecture for deep learning , GPU computing , and energy-efficient systems . His work explores memory compression , network-on-chip designs , and heterogeneous computing . Publication Trends highlight advancements in deep learning accelerators , GPU memory systems , and energy-efficient architectures . Notable themes include sparsity exploitation , multi-chip modules , and fault-tolerant GPU pipelines . Scientific Recognition : ACM Fellow IEEE Fellow Sloan Foundation Research Fellow Best Paper Awards at ASPLOS 2009 and ISPASS 2011 Laboratory Affiliations : Computer Architecture and Technology Laboratory (CART) TRIPS Project (Tera-Op Reliable Intelligently adaptive Processing System) NVIDIA Research
Cecilia Mascolo is a Professor of Mobile Systems at the University of Cambridge , specifically in the Department of Computer Science and Technology . She co-directs the Centre for Mobile, Wearable System and Augmented Intelligence and is a Fellow of Jesus College, Cambridge . Her research focuses on mobile systems , machine learning for mobile health , and earable technology . She has been awarded prestigious grants such as the ERC Advanced Research Grant (2019-2025) and the EPSRC Open Research Fellowship (2025-2030). Currently on sabbatical at Harvard University , her work bridges systems and machine learning for health applications. Education: PhD in Computer Science from the University of Bologna, Italy. Previous Affiliation: Faculty at University College London before 2008. Her research spans mobile and wearable systems for health and behavior monitoring, focusing on on-device machine learning , uncertainty-aware models , and audio-based diagnostics . Key areas include federated learning , edge computing , and respiratory disease progression analysis via wearables. She explores earable technology for physiological monitoring, gait analysis, and even toothbrushing tracking using in-ear sensors. Her recent publications highlight advancements in earable-based health monitoring , including heart rate estimation , respiratory rate detection , and ECG analysis using machine learning. She emphasizes longitudinal health data from consumer devices, advocating for scalable diagnostics beyond traditional clinical standards. Scientific Awards: ERC Advanced Research Grant EPSRC Open Research Fellowship Best Paper Award - IEEE Percom 10-Year Impact Award - ACM Ubicomp Computer Laboratory Ring Hall of Fame Best Paper Award Student: Andrea Ferlini - ACM SIGMOBILE Doctoral Dissertation Runner-up She leads the Mobile Systems Research Laboratory , mentoring a team of 15 researchers (postdocs and PhD students), and has graduated over 25 PhD students. Her teaching includes Mobile Health courses at the University of Cambridge, and she serves as Director of Studies for Computer Science at Jesus College.
Tariq Iqbal is an Assistant Professor at the University of Virginia , with joint appointments in the Department of Systems and Information Engineering and Department of Computer Science . He leads the Collaborative Robotics Lab (CRL) , specializing in human-robot teams and embodied AI . Previously, he was a Postdoctoral Associate at MIT's CSAIL , advised by Prof. Julie Shah , and earned his Ph.D. in Computer Science from University of California San Diego (UCSD) under Prof. Laurel Riek . Ph.D. in Computer Science, University of California San Diego (2017) M.S. in Computer Science, University of Texas at El Paso (2012) B.S. in Computer Science and Engineering, Bangladesh University of Engineering and Technology (2007) His research lies at the intersection of artificial intelligence and robotics , focusing on human-robot collaboration in dynamic environments. Key areas include motion prediction , multimodal fusion , trust modeling , and collaborative learning . His work integrates cognitive science and deep learning to enhance robotic fluency in naturalistic settings. Recent publications (2023–2025) highlight advancements in human-robot team dynamics , multimodal dataset creation , and motion prediction algorithms . Notable works include Energy-Based Transformers for scalable AI, PoseTron for motion prediction, and Accessible Navigation Mapping for assistive robotics. These contributions span trust modeling , cloud robotic infrastructure , and safety in close-proximity collaboration . National Science Foundation (NSF) CAREER Award Air Force Office of Scientific Research (AFOSR) Young Investigator Program (YIP) Award Commonwealth Center for Advanced Manufacturing (CCAM) Innovation Award As faculty, he has secured grants from NSF and AFOSR , mentored research students, and taught courses like Stochastic Modeling I (SYS 6005) and Robots and Humans (SYS 4582/6465, ECE 4502/6465, CS 6465) . His prior industry roles at IBM Watson Lab and Grameenphone Ltd. inform his applied research in telecom infrastructure and cognitive robotics . He leads the Collaborative Robotics Lab (CRL) at UVA, which develops multimodal datasets , real-time coordination algorithms , and adaptive pathfinding systems . Current projects explore human motion prediction , team synchrony , and embodied question-answering , reflecting his commitment to advancing human-robot fluency and contextual AI .
Ziran Wang is an Assistant Professor in the Department of Civil Engineering at Purdue University's College of Engineering, appointed as new faculty in 2022. His research bridges digital twin technologies, autonomous driving systems, and human-machine interaction to advance intelligent transportation solutions. Ph.D. in Mechanical Engineering, University of California, Riverside Prior role: Principal Researcher at Toyota North America His work focuses on creating personalized autonomous driving experiences through machine learning, emphasizing safety and efficiency in real-world applications. Key areas include multimodal large language model integration, federated learning for privacy-preserving data sharing, and cooperative perception frameworks. He develops novel approaches for digital twin-based traffic simulation, medical emergency detection in vehicles, and human behavior modeling in complex urban environments. Analysis of his 2024-2025 publications reveals a dominant trend toward generative AI applications in autonomous driving, particularly for perception-prediction-planning integration and real-world validation. His research increasingly incorporates digital twins for safety-critical testing and explores medical applications through in-vehicle health monitoring systems. Dr. Wang advises graduate students including Wenhui Huang and leads the Purdue Digital Twin Lab, which develops advanced simulation and testing platforms for autonomous systems. His lab maintains strong industry partnerships with Toyota for real-world deployment and validation of research成果.
