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Tal MalkinView profile

Professor

• Cryptography
• Computer Security
• Complexity Theory
• +8 more

Tal Malkin is a Professor of Computer Science at Columbia University, directing the Cryptography Lab and serving as inaugural chair of the Cybersecurity Center at Columbia's Data Science Institute. She holds a Ph.D. from MIT (2000), joined Columbia after AT&T Labs research experience, and focuses on cryptography, security, complexity theory with applications in secure computation, zero-knowledge proofs, and privacy-preserving systems . Education: B.S. in Math and Computer Science, Bar-Ilan University M.S. in Computer Science, Weizmann Institute of Science Ph.D. in Computer Science, MIT (2000) Research Interests: Malkin's work spans foundational and applied cryptography, including homomorphic encryption, lattice-based protocols, attribute-based encryption, and tamper-resilient systems . She explores intersections with machine learning and information theory , addressing challenges in multi-party computation , public-key encryption , and side-channel resistance . Scientific Contributions: Her publications include breakthroughs in non-malleable codes , secure computation , and privacy-preserving databases . Notable works involve continual leakage resilience , optimally-fair coin tossing , and garbled circuits for efficient cryptographic protocols. Awards & Grants: Recipient of the NSF CAREER award, IBM and Google faculty research awards, and the IACR Fellow designation. Her research is funded by NSF, NSA, DHS, NYSIA, IARPA, and industry partnerships with Google, IBM, Mitsubishi, and NEC. Professional Leadership: Former conference chairs at CRYPTO 2021 , CCS 2017 , and CT-RSA conferences. Active in program committees for over 20 leading cryptography and security events, including FOCS , Eurocrypt , and Real World Crypto . Advising: Supervised numerous Ph.D. students and postdoctoral researchers, including Marshall Ball , Chengyu Lin , and Negev Shekhel Nosatzki . Her lab has mentored graduates like Ghada Almashaqbeh (2019) and Fernando Krell (2016), focusing on decentralized networks , secure learning , and cryptographic primitives .

Fei TengView profile

Associate Professor

• Power Systems
• Cybersecurity
• Renewable Energy Integration
• +3 more

Dr. Fei Teng is an Associate Professor in the Department of Electrical and Electronic Engineering at Imperial College London and Director of Education at the Energy Futures Lab, a pan-university hub for interdisciplinary energy research. He holds visiting positions at MINES ParisTech, PolyU Hong Kong, and KTH Sweden. His research focuses on software-defined power networks, stability-constrained optimization, cyber-resilient decision-making, and privacy-preserving data exchange in low-inertia grids. Dr. Teng has authored over 100 publications in top-tier journals and conferences, with funding from EPSRC, Innovate UK, the Royal Society, and industry partners like Hitachi and National Grid ESO. His work addresses critical challenges in grid stability, cybersecurity, and renewable energy integration. He has received prestigious awards, including the IEEE Early Career Award and Royal Society Kan Tong Po Fellowship. Research interests include high-penetration power electronics, uncertainty modeling in power systems, and data privacy in smart grids. His interdisciplinary approach integrates machine learning, quantum computing, and cyber-physical systems to enhance grid resilience and efficiency. Key contributions include frameworks for stability-constrained optimization, cyber-resilient control, and synthetic inertia from electric vehicles. He advocates for software-defined grids to revolutionize decision-making frameworks for NetZero systems.

Naresh R. ShanbhagView profile

Professor

• Energy-efficient computing
• Machine learning hardware
• In-memory computing
• +7 more

Naresh R. Shanbhag is the Jack Kilby Professor in the Department of Electrical and Computer Engineering and the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign. He serves as Director of the Systems on Nanoscale Information fabriCs (SONIC) Center and held the D.J. Gandhi Distinguished Visiting Professorship at IIT Mumbai from 2015-2020. Previously, he was a visiting faculty member at National Taiwan University (2007) and Stanford University (2014). Dr. Shanbhag received his doctorate from the University of Minnesota (1993) in Electrical Engineering. From 1993 to 1995, he worked at AT&T Bell Laboratories as the lead chip architect for AT&T's 51.84 Mb/s transceiver chips over twisted-pair wiring for Asynchronous Transfer Mode (ATM)-LAN and very high-speed digital subscriber line (VDSL) chip-sets. His research focuses on the design of energy-efficient machine learning, communications, and signal processing systems on resource-constrained embedded platforms. He explores fundamental trade-offs between energy efficiency, latency and accuracy of decision-making systems implemented in nanoscale technologies, with applications to computer vision, biomedicine, automatic target recognition, and imaging. His work spans four primary focus areas: Resource-efficient Machine Learning for the Edge, In-memory Computing (IMC), Energy-efficient High Data Rate Communications, and Shannon-inspired Statistical Error Compensation (SEC). Analysis of his recent publications reveals a strong emphasis on in-memory computing architectures (SRAM, MRAM, RRAM) for machine learning acceleration. His work consistently addresses energy-accuracy trade-offs, with increasing attention to security aspects of hardware implementations and applications to MIMO signal processing and edge AI systems. His research demonstrates a progression from theoretical foundations to practical silicon implementations. 2024 Semiconductor Research Corporation Innovation Award 2018 Semiconductor Industry Association/Semiconductor Research Corporation University Researcher Award 2018 IEEE International Symposium on Circuits and Systems Best Paper Award 2006 IEEE Fellow 1996 National Science Foundation CAREER Award Professor Shanbhag has mentored over 50 graduate students who now work at leading technology companies including Qualcomm, Amazon, Nvidia, Intel, and Apple. His research has been generously supported by the National Science Foundation, DARPA, AFRL, Semiconductor Research Corporation, Texas Instruments, Sandia National Laboratories, and industry partners including IBM, GlobalFoundries, and Intel Corporation. He led the Alternative Computational Models research theme (2006-2012) and was the founding Director of the SONIC Center (2013-2017), a 5-year multi-university center funded by DARPA and SRC. Currently, he leads research themes in the SRC and DARPA funded JUMP 2.0 Program's Center for Co-Design of Cognitive Systems and the Center for Ubiquitous Connectivity, and in the NSF IUCRC Center for Advanced Semiconductor Chips with Accelerated Performance (ASAP). As Director of the Systems on Nanoscale Information fabriCs (SONIC) Center, Professor Shanbhag leads a multidisciplinary team exploring novel computing paradigms for the nanoscale era. His group has benchmarked an extensive collection of in-memory computing and digital accelerator IC designs, maintaining a publicly available IMC benchmarking repository of metrics extracted from published IC prototypes. His research philosophy integrates concepts from information theory, statistical signal processing, detection and estimation, VLSI architectures, and digital and analog integrated circuits to develop energy-efficient systems from algorithms to silicon implementations.

Tobi DelbruckView profile

Professor

• Neuromorphic Engineering
• Event-Based Vision Systems
• Silicon Retinas
• +6 more

Tobi Delbruck is a titular professor of physics and electrical engineering at ETH Zurich, where he leads the Sensors Group at the Institute for Neuroinformatics (INI) in Zurich, Switzerland. He collaborates closely with Shih-Chii Liu and Giacomo Indiveri as part of the 'hardware groups' at INI. Delbruck has also served as visiting faculty at Caltech and is a Fellow of the IEEE. His work focuses on bio-inspired and neuromorphic event-based sensory processing systems. Professor Delbruck's research spans multiple areas of neuromorphic engineering, with particular emphasis on event-based vision systems and low-power analog VLSI circuits. His work has significantly advanced the field of Dynamic Vision Sensors (DVS), which mimic the human retina's response to changes in brightness rather than capturing full frames. This approach enables extremely low-latency vision processing with minimal power consumption, making it ideal for high-speed applications and robotics. His research has applications in robotics, autonomous systems, and low-power embedded vision. Delbruck is an active contributor to the neuromorphic engineering community, co-organizing the annual Telluride Workshop on Neuromorphic Engineering and serving in leadership roles with IEEE. He has authored numerous influential publications and co-authored books including "Event-Based Neuromorphic Systems" and "Analog VLSI: Circuits and Principles." His jAER (Java Address-Event Representation) project provides open-source tools for real-time event-based sensory processing. Analysis of his recent publications shows a clear trend toward integrating event-based vision with deep learning techniques and applying these systems to practical robotics problems. His scientific achievements have been recognized with multiple awards including: IEEE Fellow Winner of Best Live Demonstration award at ISCAS 2012 Honorable Mention Award from Sensory Systems Technical Committee at ISCAS 2012 Overall Best Student Paper Award and Best Paper Award from Sensory Systems Technical Committee at ISCAS 2010 Winner of the 2006 ISSCC Jan Van Vessem Outstanding European Paper Award Professor Delbruck actively mentors students and has supervised numerous PhD and Master's theses in the areas of neuromorphic engineering and event-based vision systems. His group has secured significant research funding from various sources to support their innovative work in bio-inspired sensory processing. He teaches courses on "Electronics for Physicists II (Digital)" and "Neuromorphic Engineering," helping to train the next generation of researchers in this field. The Sensors Group at INI, which Delbruck leads, operates state-of-the-art facilities for designing and testing neuromorphic vision systems. The group maintains close collaborations with researchers worldwide and has developed several important open-source resources including the jAER project and bias generator design kits. Their work continues to push the boundaries of what's possible with event-based sensory processing, with applications ranging from high-speed robotics to low-power embedded vision systems.

Joseph A. CampbellView profile

Assistant Professor

• Explainable AI
• Robotics
• Machine Learning
• +3 more

Joseph A. Campbell is an Assistant Professor in the Department of Computer Science at Purdue University, leading the Collaborative AI for Machines and People (CAMP) Lab. He holds a Ph.D., M.S., and B.S. in Computer Science and Computer Engineering from Arizona State University. Before academia, he worked as a software engineer for five years. Prior to Purdue, he was a Postdoctoral Fellow at Carnegie Mellon University's Robotics Institute. His research focuses on explainable machine learning and robotics, particularly how agents use explanations for self-improvement and decision-making. Key areas include theory of mind in multi-agent systems, lifelong learning, and interpretable transfer learning. His work bridges robotics and AI, with applications in human-robot interaction and prosthetic control. Notable publications include advancements in reinforcement learning with language models, multi-agent collaboration frameworks, and methods for enhancing state estimation in robots. His research has been presented at top conferences like NeurIPS, EMNLP, and CoRL. Dr. Campbell maintains an active GitHub profile (joe-campbell) with repositories such as Interaction Primitives for robotics applications. His lab, CAMP, explores AI systems that collaborate effectively with humans and other machines.

Michela BecchiView profile

Associate Professor

• Computer Architecture
• Systems Software
• Heterogeneous Computing
• +5 more

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.

Ameer AbdelhadiView profile

Assistant Professor

• Hardware Acceleration
• Neurotechnology
• Reconfigurable Computing
• +4 more

Dr. Ameer Abdelhadi is an Assistant Professor in the Department of Electrical and Computer Engineering at McMaster University. His research focuses on application-specific custom-tailored computer architectures, hardware-efficient deep learning, neurotechnology, and reconfigurable computing. He holds a PhD from the University of British Columbia and has held academic positions at the University of Toronto, Imperial College London, and Simon Fraser University, alongside industry experience in semiconductor design. Education: PhD in Computer Engineering (University of British Columbia, 2016). Research Interests: Hardware acceleration for machine learning and neurotechnology Reconfigurable computing and FPGAs/ASICs Asynchronous circuits and synchronization protocols VLSI physical design and CAD algorithms Publications span high-impact venues such as IEEE Journal of Solid-State Circuits, IEEE Hot Chips, and IEEE Micro. Notable achievements include the 2017 Best Paper Award at ASYNC for work on synchronization FIFOs. Teaching includes COMPENG 4DV4 (VLSI System Design) and ELECENG 4OI6B (Engineering Design). His lab focuses on advancing hardware systems for next-generation applications in AI and biomedical engineering.

Lin ZhongView profile

Professor

• Computer Science
• Quantum Computing
• Operating Systems
• +3 more

Lin Zhong is the Joseph C. Tsai Professor of Computer Science at Yale University, leading the Efficient Computing Lab. He holds a Ph.D. from Princeton University and M.S./B.S. degrees from Tsinghua University. Previously, he served at Rice University from 2005 to 2019. His research focuses on optimizing computing efficiency, quantum error correction, operating systems, and mobile systems. Education: Ph.D., Princeton University M.S., Tsinghua University B.S., Tsinghua University Research Interests: His work spans quantum computing (e.g., decoding algorithms for surface codes), operating systems (safety, correctness, and lightweight kernels), and mobile/networking systems (massive MIMO, energy-efficient designs). Recent trends include integrating large language models (LLMs) into robotics and securing cloud-based AI workflows. Awards: NSF CAREER Award ACM SIGMOBILE RockStar (2014) and Test of Time (2022) Fellowships from IEEE and ACM Best Paper Awards at ACM MobileHCI, IEEE PerCom, ACM MobiSys, and more Lab & Teams: His Efficient Computing Lab explores systems for quantum error correction (e.g., FPGA-based decoders), secure embedded systems, and LLM-driven robotics. Projects include TimelyLLM (real-time LLM serving) and Blindfold (confidential memory management).

Nabil ImamView profile

Assistant Professor

• Computational Neuroscience
• Parallel Computing
• Probabilistic Methods
• +3 more

Nabil Imam is an Assistant Professor at the School of Computational Science and Engineering within the College of Computing at Georgia Institute of Technology. He holds a Ph.D. in electrical engineering and neuroscience from Cornell University, advised by Rajit Manohar and Barbara Finlay. Prior to academia, he conducted research at IBM and Intel Labs, focusing on neuromorphic engineering and AI. His current research integrates computational neuroscience, probability theory, and control systems to model biological computation, with an emphasis on process algebras for asynchronous circuits and systems. Education: Ph.D. in Electrical Engineering and Neuroscience, Cornell University (Advisors: Rajit Manohar, Barbara Finlay) Research interests include computational neuroscience, parallel computing, probabilistic methods, and neuromorphic systems. His work bridges biological neural mechanisms with technological applications, such as neuromorphic olfactory circuits and cortical development models. Notable contributions include neuromorphic chips featured in Science and Nature . His publications highlight interdisciplinary trends in neural coding, neuromorphic hardware, and evolutionary neuroscience. Recent work explores dual computational systems in mammalian brain evolution and self-organizing cortical structures. Earlier projects include scalable spiking-neuron integrated circuits (Science, 2014) and neurosynaptic cores with event-driven architectures (Best Paper Award, 2012). Awards: Best Paper Award at IEEE International Symposium on Asynchronous Circuits and Systems (2012) Teaching includes CSE 8803: Computational Methods for Complex Systems. His lab investigates process algebra frameworks for asynchronous systems and biological computation principles. Collaborations span industry (IBM, Intel) and academic institutions. Future directions emphasize theoretical neuroscience and neuromorphic technology applications.

Joseph FriedmanView profile

Associate Professor

• Artificial Intelligence Hardware
• Beyond-CMOS Computing
• Neuromorphic Computing
• +6 more

Joseph S. Friedman is an Associate Professor of Electrical & Computer Engineering at the University of Texas at Dallas, leading the NeuroSpinCompute Laboratory within the Erik Jonsson School of Engineering and Computer Science. His research focuses on unconventional computing paradigms leveraging nanotechnology, including neuromorphic systems, spintronics, and memristive devices. He specializes in nanomagnet-based logic architectures, neuromorphic computing with domain walls and skyrmions, and hardware security for emerging technologies. His research explores energy-efficient computing through novel paradigms such as reversible skyrmion logic, neuromorphic networks using magnetic tunnel junctions, and stochastic Bayesian inference circuits. He has pioneered spintronic neurons demonstrating 94% accuracy in handwritten digit recognition and developed secure logic locking mechanisms using nanomagnet logic. His work integrates experimental fabrication with SPICE modeling, emphasizing scalable beyond-CMOS systems. Recent advancements include toggle SOT-MRAM architectures, quantum circuit design for neutral atom systems, and neuromorphic networks leveraging superconducting flux quanta. He advises over 20 graduate and undergraduate students, fostering innovation in AI hardware and unconventional computing. Notable projects include the NeuroSpinCompute Lab's domain wall neuromorphic networks, secure logic locking schemes, and collaborations with institutions like Sandia National Labs on neuromorphic reservoir computing. Current research trends emphasize low-energy spintronic architectures, hybrid quantum-classical systems, and neuromorphic applications in edge computing. His research is supported by NSF grants CCF-1910800 and CCF-2146439, focusing on neuromorphic and spintronic systems. He regularly contributes to conferences like IEEE Rebooting Computing and SPIE Spintronics, showcasing breakthroughs in nanomagnetic logic and neuromorphic inference.

Bing YanView profile

Assistant Professor

• Power System Optimization
• Grid Integration of Renewables
• Microgrid Operation
• +3 more

Bing Yan is an Assistant Professor in the Department of Electrical and Microelectronic Engineering at Rochester Institute of Technology (RIT), affiliated with the Kate Gleason College of Engineering. She holds a B.S. in Information Management from Renmin University of China (2010), and M.S. and Ph.D. degrees in Electrical Engineering and Statistics from the University of Connecticut (2012–2017). Prior to RIT, she was an Assistant Research Professor at the University of Connecticut. Dr. Yan’s research focuses on power system optimization , including grid integration of renewables (wind/solar), microgrid operations, distributed energy systems, and manufacturing scheduling. She has published over 30 peer-reviewed articles and secured grants from the National Science Foundation (including a CAREER Award), Department of Energy, and industry partners like Brookhaven National Laboratory and ABB. Her work emphasizes mixed-integer linear programming and machine learning applications in energy systems. Notable contributions include stochastic unit commitment models for wind farms, voltage control via deep reinforcement learning, and multi-layer weather models for PV prediction. She advises on projects involving grid resilience, smart manufacturing, and data-driven optimization. Awards: National Science Foundation Faculty Early Career Development (CAREER) Award Multiple NSF grants, DOE grants, and industry contracts Teaching: Courses include Circuits I , Electric Power Transmission & Distribution , and Advanced Power Systems . She also mentors students through co-op programs and independent studies. Labs/Teams: Leads the Intelligent Lab of Power and Manufacturing (ILPM), focusing on multidisciplinary solutions for energy and manufacturing systems. The lab emphasizes hands-on training and innovation in smart grid technologies and sustainable energy systems.

Mark R. GreenstreetView profile

Professor

• Formal Verification
• VLSI Design
• Analog Circuit Verification
• +3 more

Mark R. Greenstreet is a Professor in the Department of Computer Science at the University of British Columbia (UBC). He holds a BSc from Caltech (1981), MA (1988), and PhD (1993) in Computer Science from Princeton University. His primary research focuses on formal verification of analog and mixed-signal (AMS) circuits, VLSI design, and hybrid systems. Notable contributions include the STARI signaling technique, tools like Coho for reachability analysis, and PReach for parallel model checking. He has advised numerous graduate students and collaborators, including Brad Bingham, Chao Yan, and Yan Peng. His work has been recognized with a Best Paper Award at the ASYNC Symposium. Supported by NSERC, Intel, and Oracle, his research bridges theoretical foundations and practical challenges in circuit design and verification. He teaches courses on formal methods, computer architecture, and automata theory at UBC.

• Superconducting Logic
• Bio-based Architectures
• Novel Computing Mediums
• +6 more

Dr. Jennifer Volk is an Assistant Professor at the College of Engineering, University of Wisconsin-Madison, specializing in Electrical & Computer Engineering. Her research focuses on leveraging novel technologies like superconductor electronics and photonics to create efficient systems for datacenters, neuromorphic computing, quantum computing, and space/sensing applications. She employs a holistic approach spanning circuit design, materials science, and computer microarchitecture. PhD (2024), University of California, Santa Barbara BS (2016), University of California, Santa Cruz Her research interests include superconducting logic , bio-based architectures , and novel computing mediums , emphasizing co-optimization of logic and circuit blocks. Her work develops design abstractions to simplify adoption of unconventional technologies. Dr. Volk's publications demonstrate expertise in superconducting circuit design, radiation-hardened CMOS for particle physics, and photonic materials. She has received numerous awards including the 2025 John D. Wiley Assistant Professorship and IEEE fellowships in applied superconductivity. 2025 John D. Wiley Assistant Professorship 2024 UC Santa Barbara President's Dissertation Year Fellowship 2023 IEEE CSC Graduate Study Fellowship in Applied Superconductivity 2022 IEEE Micro Top Picks Honorable Mention 2021 IEEE Micro Top Picks She teaches E C E 340 - Electronic Circuits I (Spring 2025). Her work bridges materials science, circuit design, and system architecture to enable next-generation computing platforms.

Eugenio CantatoreView profile

Professor

• Flexible Electronics
• Organic Thin Film Transistors
• Ultra-low Power Micro-systems
• +4 more

Eugenio Cantatore is a Full Professor in the Integrated Circuits group at Eindhoven University of Technology (TU/e), where he leads the Emerging Technologies Lab. His research focuses on the design and characterization of electronic circuits fabricated with emerging technologies, as well as the design of ultra-low power micro-systems for biomedical applications. Education: MSc in Electrical Engineering (1993) - Politecnico di Bari, Italy PhD in Electrical Engineering (1997) - Politecnico di Bari, Italy Cantatore's research primarily centers on flexible electronics fabricated on plastic foil, including sensors interfaces, analog-digital converters and transceivers. His work enables the integration of sensors in wearables for improved well-being and seamless medical diagnostics, and can even be embedded in food packaging to measure grocery quality and reduce waste. His research spans multiple disciplines including semiconductor physics, circuit design, and biomedical engineering, with a strong focus on practical applications of emerging electronic technologies. He has made significant contributions to organic thin-film transistor amplifiers and printed flexible electronics. His publications demonstrate a consistent progression from fundamental circuit design with organic transistors to increasingly sophisticated integrated systems with practical healthcare applications. Recent work emphasizes biomedical applications like ECG monitoring systems and ultra-low power sensor interfaces, showing his commitment to translating research into real-world solutions that address societal challenges in healthcare and sustainability. Scientific Awards: IEEE Fellow (2016) - for contributions to the design of circuits with organic thin film transistors Cantatore has been actively involved in professional service, having chaired the Technology Directions subcommittee of ISSCC from 2013 to 2016 and served as ISSCC program chair in 2019. He is currently a member of the ISSCC Executive Committee, member at large of the SSCS AdCom, Associate Editor of TCAS1, and Editor in Chief of the Open Journal of the Solid-state Circuits Society. He has authored or co-authored more than 200 papers in journals and conference proceedings, and 13 patents or patent applications. He leads the Emerging Technologies Lab at TU/e, which works closely with the Center for Care & Cure Technology Eindhoven and the Center for Quantum Materials and Technology Eindhoven. His research bridges fundamental semiconductor physics with practical applications in healthcare technology, environmental sustainability, and next-generation flexible electronic systems.

Ramana VinjamuriView profile

Associate Professor

• Brain-Machine Interfaces
• Neuroprosthetics and Exoskeletons
• Machine Learning
• +4 more

Ramana Vinjamuri is an Associate Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He holds a secondary appointment as Visiting Professor at the Indian Institute of Technology, Hyderabad, India. His academic journey includes a Ph.D. in Electrical Engineering from the University of Pittsburgh (2008), M.S. in Bioinstrumentation from Villanova University (2004), and B.Tech. in Electrical and Electronics Engineering from Kakatiya University (2002). Dr. Vinjamuri's research focuses on Brain-Machine Interfaces (BMIs) for upper-limb prostheses control , neuroprosthetics and exoskeletons , machine learning in motor control , and neurophysiological signal processing . His work extends synergy-based models to control 37-dimensional hand movements, addresses human-robot interaction through emotionally intelligent systems, and develops neurotechnologies for substance use disorder using wearable sensors and AI. NSF CAREER Award (2019) NSF IUCRC BRAIN Center Planning Grant (2020) Harvey N Davis Distinguished Teaching Assistant Professor Award (2018) His publications demonstrate expertise in EEG and EMG signal analysis , deep learning for motor decoding , synergy modeling , and humanoid robot control . The Vinjamuri Lab at UMBC involves graduate, undergraduate, and high school researchers, with international collaborations in India and the US.