Find Academic Supervisors

Yang SuiView profile

Researcher

• Efficient AI
• Machine Learning Systems
• Model Compression
• +5 more

Yang Sui is a Postdoctoral Research Associate in the Department of Computer Science at Rice University, collaborating with Professors Xia (Ben) Hu and Hanjie Chen. His research focuses on Efficient AI and Trustworthy AI, including deep neural networks, large language models (LLMs), diffusion models, and algorithm-hardware co-design. He holds a PhD from Rutgers University (2024), an MS from Jilin University (2019), and a BS from Jilin University (2016). Education: PhD, Computer Science, Rutgers University, 2024 MS, Computer Science, Jilin University, 2019 BS, Computer Science, Jilin University, 2016 Research Interests: Efficient AI: Model Compression (pruning, quantization, low-rank decomposition), Generative AI (diffusion models, LLMs), and algorithm-hardware co-design. Trustworthy AI: Adversarial robustness (backdoor attacks, vulnerability detection). He has interned at Snap Research (2024), Tencent America (2022), and Baidu (2018), contributing to projects like BitsFusion quantization and Paddle-Lite framework. Awards: Paul Panayotatos Scholarship (2024) Best Paper Runner-Up Award (DCAA Workshop at AAAI 2023) First Place in ESWEEK Classification Track (2023) SGS Travel Award (2023) Advising & Grants: Advises students on topics like LLM quantization and multimodal models. Collaborates with industry and academia on grants related to efficient AI and hardware co-design. Led projects like Rice’s “Efficient Deep Learning Reading Group” (2023). Labs & Teams: Contributes to Snap’s Creative Vision team, Rutgers’ research groups, and co-design initiatives with industry partners like Baidu and Tencent.

• Neuroscience
• Consciousness Studies
• Neuroimaging
• +4 more

Aya Khalaf is an Associate Research Scientist at the Yale School of Medicine, affiliated with the Blumenfeld Lab and the Janeway Society. Her research focuses on understanding neural mechanisms of consciousness, brain-computer interfaces, and machine learning applications in healthcare. She collaborates with leading institutions and researchers globally on studies involving EEG, fTCD, and neuroimaging techniques. Key research interests include auditory and visual perception networks, impaired consciousness in epilepsy, and hybrid BCI systems. Her work bridges cognitive neuroscience with engineering, aiming to translate findings into clinical tools. Notable collaborations include projects with Hal Blumenfeld, Dennis Spencer, and international teams testing consciousness theories. Publications highlight advancements in neural activity analysis, BCI calibration optimization, and multimodal signal processing. Her contributions span theoretical neuroscience frameworks and applied biomedical engineering solutions.

• Health Economics
• Applied Microeconomics
• Industrial Organization

Assoc Prof Yang Zhang is an Associate Professor in the School of Economics at the National University of Singapore , Department of Economics. He has been affiliated with the university since 2012, progressing from Lecturer (2012-2015) to Senior Lecturer (2016-2021) and currently Associate Professor since 2022. Education: PhD in Managerial Economics and Strategy (Northwestern University, 2011) BA in Economics (Peking University, 2005) Research Interests: Health Economics Applied Microeconomics Industrial Organization Recent Research Trends focus on leveraging deep learning and structural biology tools for protein structure prediction, RNA modeling, and computational drug design. His work integrates advanced algorithms with large-scale metagenomic data to improve accuracy in complex biological system modeling.

Linus WunderlichView profile

Lecturer

• Mathematical Finance
• Machine Learning
• Deep Neural Networks
• +6 more

Dr. Linus Wunderlich is a Lecturer in Financial Mathematics at Queen Mary University of London (QMUL), affiliated with the School of Mathematical Sciences. He holds a PhD in Hybrid Finite Element Methods for Non-linear and Non-smooth Problems in Solid Mechanics from Technical University Munich. His research focuses on integrating machine learning with numerical techniques in mathematical finance, including solving high-dimensional PDEs via neural networks and accelerating risk computations through Chebyshev interpolation. He also explores the theoretical foundations of AI and its applications in finance, engineering, and education. Linus teaches courses such as Mathematical Tools for Asset Management and Masterclass in Business Analytics , emphasizing practical machine learning skills for business contexts. His work spans computational finance, numerical analysis, and interdisciplinary projects including Olympic medal prediction models and violin bridge modeling from CT scans. Research grants include a £10,800 project with Numerical Algorithms Group Ltd on efficient volatility interpolation (2018–2019). He collaborates with institutions like LSE, Birkbeck, and University College Dublin, presenting at conferences such as SIAM Financial Mathematics and the German Probability & Statistics Days. His activities include developing interactive art projects and teaching machine learning courses during the pandemic. No scientific awards are explicitly mentioned in the provided texts. His research interests and publications reflect a blend of mathematical rigor and applied innovation, bridging finance, engineering, and education.

Graham RileyView profile

Lecturer

• High Performance Computing
• Scientific Computing
• Parallel Computing
• +4 more

Graham Riley is a Lecturer in the School of Computer Science at the University of Manchester and holds a part-time position in the Scientific Computing Department (SCD) at STFC, Daresbury. His research focuses on high performance computing (HPC), software engineering for scientific computing, and performance modeling for parallel machines. Key areas include techniques for developing HPC applications, software architectures for coupled modeling, and performance control in distributed systems. His work emphasizes collaboration with computational scientists in domains such as Earth System Modelling (e.g., UK Met Office), computational chemistry, and biology. He has contributed to projects like the EuroExa architecture for exascale computing and the LFRic weather/climate model porting to FPGAs. Riley's research also explores energy efficiency in HPC systems and FPGA acceleration strategies for scientific workloads. Notable contributions include studies on parallelization strategies, FPGA-based acceleration of climate models, and optimizing OpenCL for heterogeneous architectures. His work aligns with UN Sustainable Development Goals, particularly through contributions to climate modeling and sustainable computing practices. Riley collaborates with institutions like the Met Office and the ESM community in Europe/US. His academic profile reflects a balance between theoretical research and practical application, with a strong focus on bridging computational science and engineering challenges.

David H. AlbonesiView profile

Professor

• Computer Architecture
• Power-Efficient Systems
• Reconfigurable Architectures
• +5 more

David H. Albonesi is a Professor in the Computer Systems Laboratory at Cornell University's School of Electrical and Computer Engineering. His research focuses on power-efficient computer architectures, including reconfigurable systems, accelerator design for deep learning, smart buildings, and silicon nanophotonics interconnects. He has held leadership roles in major conferences like ISCA and MICRO, and serves on editorial boards for IEEE Computer and IEEE Micro. Research interests span adaptive architectures for dynamic power management, sparse matrix/tensor accelerators, and energy-efficient smart building systems. His work bridges hardware-software co-design, emphasizing phase-aware resource allocation and energy minimization. Awards include the IEEE Fellow distinction, NSF CAREER Award, and multiple teaching accolades from Cornell. Over 30 years of industry-academic collaboration has led to innovations in GALS microarchitectures, clustered multi-threaded processors, and thermal-aware scheduling. Key contributions include the CuttleSys reconfigurable multicore framework, MatRaptor sparse matrix accelerator, and foundational work in silicon photonics for on-chip interconnects. Patents cover dynamic core power management and adaptive microprocessor designs.

Peter HommelhoffView profile

Professor

• Dielectric Laser Acceleration
• Nanophotonics
• Ultrafast Electron Dynamics
• +4 more

Peter Hommelhoff is a Professor in the Chair of Laser Physics at Friedrich-Alexander University Erlangen-Nürnberg (FAU) . His research focuses on dielectric laser acceleration , nanostructured electron sources , and quantum nanophotonics . Key Research Areas: Quantum-coherent control of free electrons Attosecond electron pulse generation Ultrafast dynamics in 2D materials (graphene, hexagonal systems) On-chip photonic particle acceleration Light-driven electron emission from nanotips Quantum interference in electron-photon interactions Recent Publications highlight advancements in dielectric laser accelerators (Nature, 2023), auto-ponderomotive beam control (Phys. Rev. Lett., 2024), and non-classical electron emission (Nature Physics, 2024). His work also explores graphene valley control and Bloch electron interferometry for material band-structure analysis. Laboratory Context: The Chair of Laser Physics at FAU investigates nanostructured electron sources , photonic control of charged particles , and quantum applications in electron microscopy and sensing. Collaborations span quantum nanophotonics , attosecond science , and integrated photonic circuits .

• Dental biomechanics
• Instrumentation design
• Systems engineering
• +6 more

Dr. Jie Chen is a Professor and Chair of the Department of Mechanical Engineering at Purdue University (Indianapolis campus, with a courtesy appointment in West Lafayette). He holds dual roles in Mechanical Engineering and Orthodontics. His research focuses on dental biomechanics, energy systems, and instrumentation design, with significant contributions to orthodontic load systems and biomedical engineering. Education: B.S. in Mechanical Engineering, Tianjin University (1982) M.S. in Biomedical Engineering, Shanghai Second Medical University (1985) Ph.D. in Mechanical Engineering and Mechanics, Drexel University (1989) Research Interests: Energy efficiency in HVAC systems and electrical demand forecasting Quantification of orthodontic force systems using finite element analysis Biomechanical validation of orthodontic appliances CT-based quantification of dental displacement and root resorption Optical and haptic interfaces for medical and automotive systems His publications span energy optimization, orthodontic biomechanics, and human-machine interaction. Notable works include real-time energy management platforms and finite element modeling for orthodontic simulations. Recent research explores AI-driven visual analytics and autonomous vehicle systems. Grants & Labs: Active in interdisciplinary collaborations at Purdue, with lab work focused on biomechanical testing and smart energy systems. Supervises projects in orthodontic engineering and advanced manufacturing.

• Numerical Techniques
• Modeling & Simulation
• Optimization
• +3 more

Jordan Smart is an Assistant Professor in the Department of Aerospace Engineering at the University of Illinois Urbana-Champaign, affiliated with The Grainger College of Engineering. His roles include teaching and research in aerospace systems design and computational methods. He holds a Ph.D. and M.Sc. from Stanford University (2023 and 2018) and a B.Sc. in Mechanical Engineering from Rutgers University (2015). Prior to academia, he worked as a Mechanical Engineer at Lockheed Martin Space Systems (2016-2017) and co-founded Stargazer Design Technologies Inc. (2022-present) and Aerospace Research Community LLC (2023-present). His research focuses on numerical techniques, optimization, AI/ML-driven design, and aerospace systems modeling. Key areas include simulation acceleration, uncertainty quantification, and generative AI for configuration design. Recent work includes DeepSPACE (2024) and studies on SUAVE software optimization (2021-2023). His articles span space policy, neural network applications, and propulsion system aerodynamics. Education: Ph.D., Aeronautics and Astronautics, Stanford University, 2023 M.Sc., Aeronautics and Astronautics, Stanford University, 2018 B.Sc., Mechanical Engineering, Rutgers University, 2015 He has received multiple honors, including the NSF Graduate Research Fellowship (2018) and JEDI Service Award (2022). Professional memberships include AIAA and Sigma Xi. Courses taught include Aerospace Systems Design I/II (AE 442/443).

Guillermo Payá VayáView profile

Professor

• Embedded Systems
• Processor Architecture
• FPGA Design
• +5 more

Prof. Dr.-Ing. Guillermo Payá Vayá leads the Chair for Chip Design for Embedded Computing at Technical University of Braunschweig's Faculty of Electrical Engineering, Information Technology, and Physics. His research focuses on processor architecture design, FPGA/ASIC implementations, and optimization techniques for embedded systems, particularly in high-performance, low-power, and radiation-hardened computing domains. Primary research interests include: Application-Specific Instruction Set Processors (ASIPs) and compiler co-design Radiation effects characterization and fault-tolerant hardware Ultra-low-power processor architectures for embedded AI Hardware acceleration of neural networks and computer vision algorithms Memory subsystem optimization and parallel computing techniques Recent publications demonstrate strong emphasis on radiation-hardened electronics (35% of recent works), AI accelerator design (27%), and ultra-low-power systems (20%), with growing interest in biomedical applications. Experimental validation through FPGA prototyping and semiconductor testing is a consistent methodology across research domains. Leads research team investigating: Radiation-tolerant FPGA architectures (Trumann, Weide-Zaage) Vector processor optimization (Gesper, Thieu) Nano-scale controller design (Weißbrich) AI-hardware co-design (Kautz, Beyer)

Seetal PotluriView profile

Assistant Professor

• Trustworthy Machine Learning
• Hardware Security
• FPGA Virtualization
• +2 more

Seetal Potluri is Assistant Professor in the Department of Electrical & Computer Engineering at the University at Albany, SUNY's College of Nanotechnology, Science, and Engineering. He holds a Ph.D. in Electrical Engineering from IIT Madras and researches trustworthy ML systems, FPGA security, and hardware reverse engineering. Research addresses security challenges in hardware-accelerated AI systems, focusing on: 1) Model extraction vulnerabilities in neural network hardware; 2) Secure FPGA virtualization for cloud environments; 3) Side-channel attacks on cryptographic hardware; 4) Explainable ML for hardware security validation. Work bridges machine learning theory with hardware implementation security. Publications demonstrate consistent focus on hardware-level attack vectors (scan-chain exploitation, cache attacks) and defensive strategies (obfuscation, formal verification). Recent work increasingly targets cloud FPGA security and post-quantum cryptography implementations. Research contributions have been recognized with best paper awards at leading hardware security venues. Awards & Honors: Best Paper Recognition Award, IEEE HOST 2022 Runner-up, IEEE TTTC Doctoral Thesis Contest 2015 (Asia) Professional service includes technical program committees for DAC and ICCAD conferences. Teaching and research activities are based in CNSE Downtown 305E, with contact via spotluri@albany.edu or 518-956-8248.

Xiaozhe HuView profile

Professor

• Scientific Computing
• Numerical Analysis
• Multigrid Methods
• +7 more

Xiaozhe Hu is a full-time Professor in the Department of Mathematics at Tufts University since July 2024. Previously held positions include Associate Professor (2019-2024) and Assistant Professor (2014-2019) at Tufts, and Adjunct Associate Professor (2020-2022) at the University of Bergen, Norway. Education : PhD in Computational Mathematics (Zhejiang University, 2009); BS in Information and Computer Science (Zhejiang University, 2004) His research focuses on scientific computing and numerical analysis , particularly: Development of adaptive and parallel numerical methods for PDEs and graph problems Multigrid/multilevel solvers for large-scale coupled systems Quantum algorithms and spectral graph theory Applications in poromechanics , reservoir simulation , and bioinformatics Recent publications demonstrate expertise in preconditioning techniques for Biot’s model, meshless methods for fluid-structure interaction, and data-driven discretization approaches . Awards include the Reimann-Louville Award (2016) and outstanding PhD graduate recognition (Zhejiang Province, 2009). PhD Students : Junyuan Lin (Loyola Marymount), Peter Ohm (RIKEN), Casey Cavanaugh (LSU), Kaiyi Wu, Eoghan O'Keefe Master Students : Charles Colley (Purdue PhD), Yue Shen (Florida State PhD), Samuel Rabinowitz, Phong Huang, Samuel Hocking Co-organizer of the Computational and Applied Mathematics Seminar and contributor to open-source HAZmath finite element library.

Aritra DuttaView profile

Assistant Professor

• Machine Learning
• Federated Learning
• Optimization
• +7 more

Aritra Dutta is an Assistant Professor at the AI Initiative of the University of Central Florida (UCF), primarily affiliated with the Department of Mathematics and secondarily with the Department of Computer Science. He also holds an affiliation with the Pioneer Centre for AI (P1), Denmark. His research focuses on optimization (stochastic/nonconvex), distributed computing (including federated learning), numerical linear algebra, machine learning, low-rank approximation, and computer vision applications such as image/video analysis and object detection/tracking. Dr. Dutta’s work emphasizes interdisciplinary approaches, blending mathematical rigor with computational efficiency. He actively seeks Ph.D. students and postdocs with strong foundations in mathematics (optimization, linear algebra) or computer science (ML, distributed systems), prioritizing candidates with publication records in top-tier venues. His teaching includes courses in applied mathematics and computational methods. His research outputs span communication-efficient distributed learning frameworks, convergence analysis of optimization algorithms, and vision transformer architectures. Notable projects include GAEA (geolocation-aware conversational models) and MAVREC (multi-view aerial visual recognition). Postdoc opportunities: Open for exceptional candidates. Labs/Initiatives: UCF AI Initiative (UCF Aii), UCF Center for Research in Computer Vision (CRCV). Grants: Competitive research assistantships with tuition support.

• Antenna Engineering
• 6G/5G Communications
• Metamaterials
• +6 more

Ming Shen is an Associate Professor at the Department of Electronic Systems, part of The Technical Faculty of IT and Design at Aalborg University. His research focuses on antennas, millimeter-wave systems, and AI-driven RF sensors with applications in 5G/6G communications, biomedical engineering, and smart systems. His research interests span antenna design (including phased arrays, metamaterials, and compact structures), AI integration in electromagnetic systems, and medical sensor technologies. Recent projects include drone-based electromagnetic signature analysis, vibration energy harvesting for pacemakers, and smart healthcare systems for posture recognition and surgical site infection monitoring. Key projects include DRONES: Drone-Obtained Electromagnetic Signatures (2024–2028), Sensor Intelligence for Healthcare and Sports (2022–2027), and DeepBone (2021–2022), which explored deep learning for surgical infection detection. His work also bridges machine learning and electromagnetic design, with breakthroughs in surrogate modeling and automated antenna optimization. Ming Shen has supervised 6 PhD students and published over 160 peer-reviewed articles. Notable contributions include AI-assisted NLOS sensing, ultra-wideband antenna innovations, and medical applications such as electrical impedance-based bone healing assessment.

Ruohong JiaoView profile

Assistant Professor

• Thermochronology
• Orogenic Processes
• Landscape Evolution
• +1 more

Dr. Ruohong Jiao is an Assistant Professor in Earth and Ocean Sciences at the University of Victoria, currently on leave. Specializing in fission-track and (U-Th)/He thermochronology, his research investigates orogenic processes and landscape evolution through field studies and computational modeling. Research focuses on tectonic exhumation patterns, mountain building processes, and fluvial system responses to tectonic forcing. Recent methodological innovations include applications of physics-informed neural networks for thermochronological data inversion. Field areas span Vancouver Island, Tibetan Plateau, Tian Shan, and Yangtze River regions. Publications demonstrate expertise in reconstructing tectonic histories using low-temperature thermochronometry, with emphasis on coupling between surface processes and deep crustal dynamics. Professional service includes graduate student committee supervision.