جست‌وجوی استاد و پژوهشگر

• Biomedical Engineering
• Machine Learning
• Wearable Technology
• +۳ مورد دیگر

Muhammad Awais Bin Altaf is a researcher specializing in biomedical engineering, machine learning, and wearable technology. His work focuses on low-power embedded systems for neurological and cardiovascular monitoring, including EEG processors for seizure detection and PPG-based blood pressure classification. He has collaborated extensively with co-authors like Wala Saadeh and Jerald Yoo on IEEE journals and conferences. His research interests include Biomedical signal processing Wearable health devices Machine learning for medical diagnostics Energy-efficient hardware design Neurological disorder detection Embedded systems for clinical applications Recent publications highlight trends in shallow neural networks, autoencoders, and hardware acceleration for real-time health monitoring. Key subfields span seizure prediction, stress detection, and impedance-adaptive sensors. Collaborations include institutions in Germany, Finland, and Pakistan. His work often integrates open-source toolflows and industry-standard chip design techniques, emphasizing practical implementations for wearable environments. Contributions to HDR imaging algorithms and biomedical SoCs demonstrate interdisciplinary expertise in signal processing and healthcare technology.

• Photonics
• Fiber Optic Sensors
• High-Power Lasers
• +۱۱ مورد دیگر

Guido Perrone is a Tenured Associate Professor at the Department of Electronics and Telecommunications (DET) , Politecnico di Torino, with a focus on Photonics, Fiber Optic Sensors, and High-Power Lasers. His research bridges industrial and biomedical applications, particularly additive manufacturing and laser therapy, aligning with SDG goals like Good Health, Industry Innovation, and Clean Water. Research Interests : Photonics, Fiber Optic Sensors, High-Power Lasers, Biomedical Applications, Additive Manufacturing, Electromagnetic Fields, Signal Processing, and Machine Learning. Projects : Led competitive grants including DESIGN (2024–2028), Yellow-FLiCkEr (2022–2025), and STELLAR (2021–2022) as Scientific Director/Manager. Teaching : Course instructor for Photonics , Industrial Photonics , and Passive Optical Components at undergraduate and graduate levels. Grants & Patents : Supervised >10 research projects and holds 2 patents (national/international) on optical transducers. Students : Mentored 4 PhD candidates in laser and sensor technologies.

• Electrical Engineering
• Computer Engineering
• Wireless Sensor Networks
• +۵ مورد دیگر

Mahmut Tenruh is an Associate Professor at Muğla Sıtkı Koçman University, Faculty of Engineering, Department of Electrical and Electronics Engineering. He holds a bachelor's degree from Gazi University and a Ph.D. from the University of Sussex. His research focuses on wireless sensor networks, CAN protocols, embedded systems, and renewable energy applications. 2024: Performance analysis of photovoltaic systems in Yemen 2021: Robotics control systems and intelligent vehicle suspension design 2019: Time-triggered CAN FD protocol for real-time distributed control 2015-2014: Network modeling, solar data tracking, and scheduling optimization His work has been cited 23 times across various publications. He has supervised over 20 graduate theses and led multiple TÜBİTAK-funded projects, including remote pool control systems and wireless security solutions. TÜBİTAK Scientific Award (2011) Muğla Sıtkı Koçman University Scientific Award (2011)

Salar Fattahiمشاهده پروفایل

استادیار

• Optimization
• Machine Learning
• Robust Matrix Recovery
• +۴ مورد دیگر

Salar Fattahi is an Assistant Professor at the University of Michigan, affiliated with the College of Engineering’s Department of Industrial and Operations Engineering. He holds additional appointments with the Michigan Institute for Computational Discovery and Engineering (MICDE), Michigan Institute for Data Science (MIDAS), and the Michigan Center for Applied and Interdisciplinary Mathematics (MCAIM). PhD in Industrial Engineering and Operations Research from UC Berkeley M.Sc. in Electrical Engineering from Columbia University B.Sc. in Electrical Engineering from Sharif University of Technology Research Focus: Developing scalable computational methods for structured optimization and machine learning problems by exploiting sparsity, low-rankness, and benign landscape properties. Applications span gene regulatory networks, power systems, and brain connectivity modeling. 2025: Parametric algorithms for MIQPs over trees 2024: Triple Component Matrix Factorization for global/local/noise separation 2023: Robust subspace recovery and dictionary learning Scientific Recognition: NSF CAREER Award (2023) INFORMS Best Paper Awards (2023, 2024) Dean’s MLK Spirit Award (2024) MICDE Catalyst Grant (2021) Academic Service: Associate Editor for INFORMS Journal on Data Science; Area Chair for NeurIPS, ICML, and ICLR. Mentored students including Jianhao Ma (now Tsinghua University), Geyu Liang (Amazon), and Aaresh Bhathena. Research supported by NSF, ONR, MICDE, MIDAS, START, and DEI Faculty grants.

Venkatesh Murthyمشاهده پروفایل

استاد

• Neuroscience
• Olfaction
• Neural Circuits
• +۳ مورد دیگر

Venkatesh Murthy serves as the Raymond Leo Erikson Life Sciences Professor of Molecular & Cellular Biology at Harvard University and co-directs the Harvard Brain Science Initiative. His laboratory is housed within the Faculty of Arts and Sciences at Harvard's Biological Laboratories in Cambridge, Massachusetts. His research investigates the neural and algorithmic basis of odor-guided behaviors in terrestrial animals, primarily using mouse models. The Murthy Lab develops naturalistic behavioral paradigms that enable simultaneous electrophysiological recordings, high-resolution optical imaging, and optogenetic manipulation. Key research areas include neural circuit dynamics in the olfactory system, modification of circuits through behavioral state and learning, and development of computational models to explain neural observations. The lab maintains strong interdisciplinary collaborations with theoretical neuroscientists. Recent publications (2023-2025) reveal consistent focus on olfactory navigation mechanisms, neural signal processing algorithms, circuit-level analysis of social behaviors, and biomimetic applications for electronic sensing systems. Work spans experimental techniques including multi-animal pose estimation (using DeepLabCut), neural deconvolution methods, and analysis of fluctuating odor environments. The Murthy Lab operates within Harvard's Department of Molecular & Cellular Biology as part of the broader Harvard Brain Science Initiative ecosystem, contributing to research in Cognitive and Behavioral Neuroscience, Theory and Computation, and Sensory and Motor Systems.

Jerry Liمشاهده پروفایل

دانشیار

• learning theory
• quantum information theory
• large foundation models
• +۴ مورد دیگر

Jerry Li is an associate professor at the University of Washington's Paul G. Allen School of Computer Science & Engineering. Previously, he was a principal research scientist at Microsoft Research Redmond and was the VMware Research Fellow at the Simons Institute in Fall 2018. Li completed his Ph.D. at MIT under the supervision of Ankur Moitra and his master's degree at MIT under Nir Shavit. As an undergraduate, he also attended the University of Washington, where he worked on complexity of branching programs and hardness of learning problems in database theory and AI. His primary research interests focus on learning theory broadly defined, with specific expertise in quantum information theory, large foundation models, and high-dimensional statistics. He has a particular interest in applying analysis and analytic techniques to theoretical computer science problems. His recent work spans quantum computing, robust machine learning, and theoretical foundations of deep learning, showing a clear trend toward bridging quantum information theory with statistical learning theory. Li has made significant contributions to the fields of robust statistics, quantum computing, and theoretical machine learning, with numerous publications in top-tier conferences and journals including FOCS, STOC, NeurIPS, ICML, and Science. His work often bridges theoretical guarantees with practical applications in machine learning systems, particularly in the areas of robustness and quantum advantage. George M. Sprowls Award for outstanding Ph.D. theses in EECS at MIT Best Artifact Award at PPoPP 2015 for "The SprayList: A Scalable Relaxed Priority Queue" Communications of the ACM Research Highlights for "Robust Estimators in High Dimensions without the Computational Intractability" Invited to special issues of SIAM Journal on Computing for FOCS 2023 and STOC 2022 Spotlight Presentations at NeurIPS 2019 Notable top 5% paper at ICLR 2023 Li advises several Ph.D. students including Ziyun Chen (co-advised with Shayan Oveis Gharan) and numerous research interns. He has served on program committees for major conferences including STOC, SODA, and ITCS, and is co-organizing the FOCS 2024 Workshop on Recent Advances in Quantum Learning. His teaching includes courses such as CSE 422: Toolkit for Modern Algorithms and CSE 599-M: Robustness in Machine Learning, for which he has created publicly available video lectures.

Maria Brbicمشاهده پروفایل

استادیار

• Machine Learning
• Computational Biology
• Biomedical Informatics
• +۳ مورد دیگر

Maria Brbic is an Assistant Professor of Computer Science at EPFL, previously a postdoctoral researcher at Stanford University under Jure Leskovec. Her research focuses on developing machine learning methods for biological and biomedical applications, particularly representation learning of high-dimensional datasets, open-world semi-supervised learning, and single-cell genomics. Her work includes the STELLAR method for spatial cell type discovery (Nature Methods 2022), the ORCA framework for open-world learning (ICLR 2022), and contributions to the Fly Cell Atlas (Science 2022). She is involved in the Chan Zuckerberg Biohub and Neuro-omics projects. She received the University of Zagreb's best thesis award, was recognized as a MIT Rising Star in EECS, and won the Basel Computational Biology Conference best poster award. Her research bridges computer science with cutting-edge biomedical discovery.

• Applied Analysis
• Numerical Analysis
• Data Science
• +۶ مورد دیگر

Leon Bungert is a Professor of Mathematics of Machine Learning at the University of Würzburg, working in applied analysis and numerics with a particular focus on data science and machine learning. His research investigates PDEs and variational models on graphs, adversarial robustness of machine learning, variational regularization, and nonlinear optimization. Dr. Bungert serves as a guest editor for the European Journal of Applied Mathematics, an associate editor for Advances in Continuous and Discrete Models: Theory and Applications, and is a member of the program committee at SSVM 2025. He is also an ELLIS member and actively organizes conferences and workshops, including "MIA'25" at IHP in Paris (January 13-15, 2025), "Synergies of Machine Learning and Numerics" in Osaka (March 11-13, 2025), and "Mathematical Analysis of Adversarial Machine Learning" in Oaxaca (August 17-22, 2025). Research Interests Dr. Bungert's primary research areas include: PDEs on graphs Adversarial robustness in machine learning Inverse problems Optimization Variational problems in L-infinity Nonlinear eigenvalue problems Image reconstruction with structural priors His work bridges theoretical mathematics with practical applications in machine learning, particularly focusing on the mathematical foundations of deep learning and developing robust algorithms that can withstand adversarial attacks. He has made significant contributions to understanding the connections between partial differential equations and machine learning algorithms. Research Trends Analysis of Dr. Bungert's recent publications reveals a strong focus on the intersection of machine learning and mathematical analysis. A key theme is the application of variational methods and partial differential equations to machine learning problems, particularly in understanding and improving the robustness of neural networks against adversarial examples. His work on Lipschitz learning on graphs has established important theoretical foundations for graph-based semi-supervised learning. Additionally, his research on the infinity Laplacian and p-Laplacian equations provides deep insights into the mathematical structure of machine learning algorithms. The development of Bregman learning frameworks for sparse neural networks represents a significant contribution to efficient deep learning model training. Professional Activities Dr. Bungert is actively involved in the academic community through editorial roles and conference organization. His current professional activities include: Guest editor for the European Journal of Applied Mathematics Associate editor for Advances in Continuous and Discrete Models: Theory and Applications Member of the program committee at SSVM 2025 ELLIS member Co-organizer of multiple international conferences and workshops Technical Contributions Dr. Bungert has developed several open-source software packages that implement his theoretical contributions, including: Code for convergence rates of Lipschitz learning on graphs A Bregman training framework for sparse neural networks CLIP: Cheap Lipschitz Training of Neural Networks Nonlinear Power Method for Proximal Operators and Neural Networks Robust Image Reconstruction with Misaligned Structural Information These implementations are primarily in Python and MATLAB, demonstrating his commitment to making theoretical advances accessible for practical applications.

Zhongqi Liuمشاهده پروفایل

پژوهشگر

• Heat Transfer
• Machine Learning
• Subcooled Flow Boiling
• +۵ مورد دیگر

Zhongqi Liu, M.Sc., is a researcher at the Chair of Thermodynamics (Prof. Dongsheng Wen) at the Technical University of Munich. His work focuses on integrating machine learning with experimental heat transfer analysis for subcooled flow boiling applications. University: Technical University of Munich Department: Chair of Thermodynamics Academic Rank: Researcher Research interests include Heat Transfer , Machine Learning , Subcooled Flow Boiling , and Energy Systems . His projects aim to enhance experimental data processing and critical heat flux prediction using data-driven methods, as seen in the Boiling Image Dataset . Recent publications span biomedical imaging, cancer phototherapy, and nanotechnology, highlighting interdisciplinary applications of machine learning and optical imaging. Notable work includes optoacoustic dyes , photodynamic therapy , and nanocarrier development .

Bogdan Romanمشاهده پروفایل

پژوهشگر ارشد

• Compressed Sensing
• Signal Processing
• Sampling Theory
• +۵ مورد دیگر

Dr. Bogdan Roman is a Senior Researcher in the Centre for Mathematical Imaging in Healthcare at the Pure Mathematics department and a Visiting Research Fellow at the Computer Science and Technology department, both at the University of Cambridge. He chairs the Computer Science Admissions Test (CSAT) and coordinates the Cambridge Imaging Clinic. Research Areas : Compressed Sensing, Signal Processing, Sampling Theory, Inverse Problems, Computational Mathematics, Medical Imaging, Wireless Networks His work on compressed sensing has driven advancements in MRI resolution and sub-50nm Scanning Helium Microscopy (SHeM), funded by EPSRC. Collaborations include Siemens (MRI validation), Cambridge Radiology, and industrial partners. He has contributed to breaking the coherence barrier in imaging and developed scalable wireless access control systems. Scientific Awards : Rosetrees Interdisciplinary Prize 2016 nomination, 2nd Place at Microsoft Research Workshop 2007 He lectures Part IA Numerical Analysis and co-lectures Part III courses on Sampling and Compressed Sensing. His tools include a high-speed C++ MEX Hadamard transform and MacOH stress-testing software. His publications span mathematics, physics, and computer science disciplines.

• Ultrasonics
• Acoustic Metamaterials
• Non-Destructive Evaluation
• +۳ مورد دیگر

Professor Bruce Drinkwater is a Professor of Ultrasonics at the University of Bristol, affiliated with the School of Electrical, Electronic and Mechanical Engineering. He holds academic qualifications including a B.Eng. and Ph.D. from the University of London. His research focuses on ultrasonics, acoustic metamaterials, particle manipulation, and non-destructive evaluation, with applications in biomedical engineering, fluid dynamics, and mechanical systems. He has pioneered advancements in acoustic tweezers, levitation systems, and defect detection using ultrasonic arrays. Key research interests include the development of acoustic metamaterials for wave manipulation, optimization of ultrasonic imaging techniques, and the exploration of acoustophoretic displays. His work extends to biomedical applications such as tissue engineering and drug delivery via acoustic patterning. Recent publications demonstrate innovations in particle control, high-temperature material processing, and the integration of ultrasonic systems with robotics for pipeline inspection. Professor Drinkwater collaborates with researchers like Dr. Alexander Velichko and Dr. Jie Zhang on projects involving robotics, sensor technology, and structural health monitoring. His contributions have advanced both theoretical and applied aspects of ultrasonics, bridging the gap between fundamental research and real-world engineering challenges. No specific awards are mentioned, but his extensive publication record reflects significant scholarly impact. He advises on projects related to acoustics and mechanical engineering, though no named students are listed. His work includes the development of robots with integrated ultrasonic sensors for neurosurgery and pipeline inspection, showcasing interdisciplinary collaboration. Current initiatives involve improving ultrasonic array performance and exploring the biomedical effects of ultrasound on neuronal function.

Liang Zhangمشاهده پروفایل

استادیار

• Artificial Intelligence
• Mobile Edge Computing
• UAV Communications
• +۳ مورد دیگر

Dr. Liang Zhang is an Assistant Professor in the Department of Engineering and Aviation Sciences at the University of Maryland Eastern Shore (UMES). He holds a Ph.D. in Electrical Engineering from New Jersey Institute of Technology (NJIT) and an M.S. in Information and Communication Engineering from the University of Science and Technology of China (USTC). His research focuses on machine learning, mobile edge computing, UAV communications, wireless communications, and IoT, with an emphasis on resource optimization and algorithm design. Education: Ph.D., Electrical and Computer Engineering, NJIT (2014–2020) M.S., Information and Communication Engineering, USTC (2011–2014) B.S., Electronic Science and Technology, Huazhong University of Science and Technology (HUST) Research Highlights: Dr. Zhang has pioneered work on deep reinforcement learning algorithms for UAV-assisted edge computing, caching optimization, and latency reduction in IoT systems. His contributions include the BRIDGES testbed project at George Mason University (GMU), supported by a $2.5M NSF grant, and the development of QoE-optimized frameworks for wireless VR and airborne networks. Awards & Recognition: Outstanding Dissertation Award (NJIT, 2023) Hashimoto Prize (NJIT, 2020) IEEE GLOBECOM Travel Grant (2016) Best Paper Award (IEEE ICNC, 2014) Professional Contributions: He has published 32 peer-reviewed articles and serves as a reviewer for IEEE journals. His work integrates machine learning with networking challenges, addressing practical issues like spectrum sharing in 5G, energy-efficient VM management, and dynamic resource allocation in optical networks.

Samantha Pettiمشاهده پروفایل

استادیار

• Bioinformatics
• Computational Biology
• Machine Learning
• +۴ مورد دیگر

Samantha Petti is an Assistant Professor in both the Department of Mathematics (School of Arts and Sciences) and the Department of Computer Science (School of Engineering) at Tufts University. She is based at 177 College Avenue, Medford, MA. Her research focuses on computational biology, bioinformatics, and machine learning, with particular emphasis on protein structure analysis, genotype-phenotype mapping, and algorithm design for biological sequence analysis. She teaches courses such as Master's Thesis supervision, PhD Thesis guidance, and specialized topics in mathematics and computer science. Her work integrates interdisciplinary approaches, combining statistical methods, deep learning, and probabilistic models to address challenges in genomics, structural biology, and network science. Recent projects include developing end-to-end protein alignment tools and exploring sparse graph models for biological systems. Teaching: Supervises graduate thesis work (Master’s/PhD) and advanced courses in mathematics and computer science at Tufts. Lab/Team: Engages in collaborative research at the intersection of computational methods and biological systems.

Armin Zareمشاهده پروفایل

استادیار

• Control Theory
• Fluid Mechanics
• Stochastic Dynamical Modeling
• +۳ مورد دیگر

Armin Zare is an Assistant Professor at the University of Texas at Dallas in the Department of Mechanical Engineering , affiliated with the Center for Wind Energy . He obtained his PhD in Electrical Engineering from the University of Minnesota (2016) and previously held a postdoctoral position at the University of Southern California. Education : PhD (2016), MS (2016) in Electrical Engineering from University of Minnesota; BS (2010) in Electrical Engineering from Sharif University of Technology Research Interests : Modeling, dynamics, and control of large-scale and distributed systems; optimization; dynamics and control of complex fluid flows; renewable energy generation. His work focuses on stochastic modeling of turbulent flows and wind farm dynamics using control theory. Recent Publications highlight advancements in wind farm turbulence modeling, stochastic receptivity analysis, and covariance completion algorithms, reflecting his expertise at the intersection of control systems and fluid dynamics. Scientific Awards Young Investigator Program Award, Air Force Office of Scientific Research (2023) Doctoral Dissertation Fellowship, University of Minnesota (2015) Best Student Paper Award Finalist, American Control Conference (2014) Advisees include PhD students working on topics like wall turbulence modeling, wind farm forecasting, and stochastic dynamical systems, with several former students having completed theses on wind turbine wake modeling and fluid flow uncertainties.

Paul Millerمشاهده پروفایل

استاد

• Computational Neuroscience
• Neuroscience
• Dynamical Systems
• +۵ مورد دیگر

Paul Miller is a Professor of Biology at Brandeis University and a member of the Volen National Center for Complex Systems. His research focuses on computational neuroscience, integrating dynamical systems theory with experimental data to understand neural mechanisms underlying decision-making, memory, and sensory processing. He holds a Ph.D. from the University of Bristol (UK) and a B.A. from Cambridge University. Before joining Brandeis in 2000, he was at Georgetown University and conducted postdoctoral work in theoretical physics at Oak Ridge National Laboratory. Education: B.A., Cambridge University (1991) Ph.D., University of Bristol (1994) Research Interests: Computational Neuroscience emphasizes modeling neural circuits for short-term/long-term memory, decision-making processes, and the dynamics of neural ensemble activity. His work bridges theoretical approaches (e.g., hidden Markov modeling, spiking neuron networks) with experimental data from cortical and hippocampal systems. He explores how quasistable attractor states enable robust computation and how homeostatic mechanisms stabilize neural activity. Notable Contributions: Developed models explaining how integral feedback control enables robust sequential decision-making, elucidated synaptic mechanisms for spatiotemporal discrimination, and analyzed the role of calcium/calmodulin-dependent protein kinase II (CaMKII) in long-term memory stability. His textbook An Introductory Course in Computational Neuroscience (MIT Press) is widely used in graduate programs. Labs/Teams: Leads the Computational Neuroscience Lab at Brandeis, collaborating with experimentalists to validate theoretical models. Active in interdisciplinary projects involving neurophysiology, systems biology, and mathematical neuroscience.