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Experience + Research

– Lecturer (~ Ass. Prof.) at Queen Mary University of London ~ [2023 – ]

• Recently, joined as a Lecturer in Data Science at Queen Mary University of London. I will be teaching courses on Data Science, Biomedical Signal Processing and Machine Learning.

– Honorary Research Associate at Imperial College London  ~ [2023 – ]

• Now associated with Imperial Collage London, as an Honorary Research Associate, continuing research work on the same projects.

– Research Associate (Postdoc) at Imperial College London  ~ [2021 – 2023]

• I worked as a Research Associate (Postdoc) in National Heart and Lung Institute at Imperial College London.  The work is focus on Cardiac Function. I will be using computational skills of signal processing, machine learning and deeplearning for clinical research in this field. Working with an amazing team of clinical research, doctors and computational experts.

– Postdoctoral Research Fellow at University of East London ~ [2019 – 2021]

• Worked as a Postdoctoral Research Fellow in Intelligent Systems Research Group at University of East London. The project – Automation and Transparency across Financial and Legal Services, funded by Innovate UK, in collaboration with Intelligent Voice Ltd., and Strenuus Ltd. Work includes the analysis of the collected audio and transcribed conversations to detect the deceptive ques using linguistic and acoustic features and develop an explainable model to predict the behaviour.

– PhD from Queen Mary University of London (EECS) ~ [2015 – 2019]

• Completed PhD in Signal Processing and Machine Learning from Queen Mary University of London (UK) & University of Genova (Italy) with a joint Program. May 2019
• PhD work is focused on Predictive analysis of auditory attention from physiological signals. I designed an experiment based on the cognitive aspect of attention and collected signals (EEG, GSR, PPG) along with other active responses from 25 participants. The collected data is used for statistical analysis of responses and signal processing for designing the model for prediction of attention level.
• Supervisors: Jesús Requena Carrión (QMUL) and Francesco Bellotti (Unige).
• PhD Project webpage: https://phyaat.github.io. This webpage is designed to share all the research outcomes.

– Publications- link

Wheelhouse : Machine Learning & Signal Processing

• Second Place winner team – Urban Analytics Data Dive: Second place winner team for Urban Analytics Data Dive held at The Alan Turing Institute and hosted in collaboration with the Office of National Statistics -ONS Data Science Campus. 25-26 July 2017.
• Second Place winner team – Person Identification from Audiovisual challenge in summer school 2017 held at Centre of Intelligent Sensing, Queen Mary University of London. 7-9 Sep 2017.
• Online Competitions: I worked on Kaggle’s competitions of Machine Learning and Data Science, Profile is here https://www.kaggle.com/nikeshbajaj/competitions

• A week-long event worked with a team for Urban Analytics posed by Defense Science and Technology Laboratory (DSTL) at The Alan Turing Institute, London. 22-26 May 2017. Blog about it can be seen here.
•  An extensive week-long work on health care project – Predicting Language Outcome and Recovery After Stroke (PLORAS), a problem was posed by University College London at The Alan Turing Institute, London.  See a short summery report here. 16-20 April 2018.

• I have done several online/offline courses in signal processing and machine learning including, Deep Learning Specialization, Neural Network for Machine Learning, Probabilistic Graphical Models, and others, a list is shown in the Courses section.

• Presented seminar on ‘Ensemble approaches for classification and regression’ in summer school – Machine Learning: A computational Intelligent Approach, University of Genoa, Italy. The presentation can be found here.

• I attended Faculty Development Program on Data Analytics by Cognizant Technology Solution, India

• Python (matplotlib, numpy, scipy, pandas, scikit-learn, scikit-Image, TensorFlow, Keras), R, Matlab, C#, Java, P5js (Javascript), Chuck (Music programming language) – GithubRepo
• My python distribution libraries on PyPI :  pylfsr, regml, phyaat, spkit 

Experience:

   Teaching:

    – Teaching Assistant at Queen Mary University of London:

• Digital Signal processing -2017/18
• Coding for Scientist – 2017/18
• Data Analytics – 2017/18
• Signals and Systems Theory -2017/18
• Security Engineering -2017/18

    –  Assistant Professor:

• July 2010 – Nov 2015 :  Worked as Asst. Professor. in electronics and communication engineering department at Lovely Professional University (India), heading the digital signal processing domain. Apart from teaching and managing faculties, responsibilities included the design and conduction of signal processing courses & labs and mentoring undergraduate and master students.

    – Teaching Assistant at Aligarh Muslim University (India):

• Microprocessor and design – 2008
• Radar communication – 2009

   Other:

• Oct 2006 – July 2008  :  Worked at Colwell & Salmon Communication, Noida (India)
  
• 2005 – 2006: Worked in BPOs

Education:

• Ph.D in Machine Learning & Signal Processing from Queen Mary Univerisity of London, UK in a joint program with Unversity of Genoa. Thesis title – Predictive Analysis of Auditory Attention from Physiological Signals. Supervised by Jesus Requena Carrion (QMUL) and Francesco Bellotti (Unige). May 2019
• M.Tech (Masters) in Communication & Information Systems from Aligarh Muslim University (India). Major courses: Digital signal processing, Image processing, Speech and audio processing, Information theory & coding, Secure communications. Thesis title: Cryptanalysis of some block and stream ciphers using signal processing techniques. Supervised by Prof. Omar Farooq. 2010
• B.Tech: (Bachelors) in Electronics and Telecommunication Engineering from IETE – Institutions of Electronics and Telecommunications Engineering, New Delhi (India). 2007

Scholarships:

• JD-ICE- Joint Doctoral Course in Interactive and Cognitive Environment program for PhD between Queen Mary University of London & University of Genoa.
• Ministry of Human Resource Development (MHRD), Government of India -funding for masters

Awards & Honors:

• Microsoft Azure Data Science Research Award, $5000 cloud credits for one year, as a winning team of Data dive competition at Alan Turing Institute.
• Best Faculty Award 2014-2015 – Received Best Faculty award for outstanding performance by Cognizant Technology Solution
• Best Faculty Facilitator Award 2015  – for being facilitator for ‘Uttara-Scientia Divina’ -Student Organization at LPU
• Best Student Award at Sunrays Children High School, Itarsi, M.P. (India), 1999
• Second Prize in ‘Hindi Gyan Pariksha’ by Akshar karmi Swayam Seva Santha on Hindi Diwas, 1997

Tests:

• Graduate Aptitude Test in Engineering: GATE-2010, 2008, 2007, 2006 Qualified. GATE is conducted by Indian Institutes of Technology (IITD, IITB, IITK), that led to MHRD funding for Masters in Technology in India.
• IELTS qualified.

Member

• Associate Member of Institute of Electronics and Telecommunication Engineering – IETE, New Delhi (India)
  

Participation

• Faculty Development Program on Business Analytics in May 2014 by Cognizant Technology Services.
• Indian Robot Olympiad IRO- 2012 as a coach for a team.

Invited Talks/Seminars

• Invited as a guest speaker by Hamburg Natural Language Processing Meetup + DeepLearning.AI’s a Pie & AI community for a talk on Deception detection in the conversation using Linguistic Markers (with an exciting demo on 999 call) – Event details  + youtube link + Full Video of talk in available now. 
• Invited as a guest speaker by deeplearning.ai for Learning Community Event for NLP specialization course offered on Coursera: – Link to the recorded event
• Invited by Chandigarh University to give a tutorial session on Introduction to Machine Learning and Deeplearning for Academic Staff. Presentation slides and tutorials can be found Here
• Invited by  GLA University to talk about my Research Work –  PhyAAt
• Presented seminar on ‘Ensemble approaches for classification and regression’ in summer school – Machine Learning: A computational Intelligent Approach, University of Genoa, Italy. The presentation can be found here.

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Courses taken Online/Offline:

Machine Learning

• Machine Learning (Coursera, 2012)
• Probabilistic Graphical Models 1 (Coursera, 2016)
• Neural Network for Machine Learning (Coursera, 2017)
• Statistical Learning (Stanford Online, 2016)
• Machine Learning for Musicians and Artists (Kadenze, 2016)
• Regularization Methods for Machine Learning (Unige, 2018)
• Machine Learning: A Computational Intelligent Approach (Unige, 2018)
• Data Fusion and Bayesian Network (Unige, 2018)

Deep Learning specialization (2018)

• Neural Network and Deep Learning
• Improving Deep Neural Networks: Tuning Hyperparameters
• Structuring Machine Learning Projects
• Convolutional Neural Networks
• Sequence Models

Natural Language Processing specialization (2021)

• Probabilstic Models, Classification and Vector Space,
• Sequence and Attention Models

Signal processing

• Digital Signal Processing (Coursera, 2013)
• Audio Signal Processing for Music Applications (Coursera, 2013)
• Image and video processing (Coursera, 2013)
• Introduction to Digital Sound Design (Coursera, 2013)
• Computer Vision (Coursera, 2014)
• Computational photography (Coursera, 2013)

Fundamental courses and other courses for Fun

• Cryptography -I (Coursera, 2012)
• Applied Cryptography, Science of Secretes (Udacity, 2012)
• Control of Mobile Robots (Coursera, 2013)
• An Introduction to Interactive Programming in Python (Coursera, 2012)
• Learn To Program: Fundamental (Coursera, 2013)
• The Data Scientist’s Toolbox (Coursera, 2015)
• Algorithms: Design and Analysis, Part 1 (Coursera, 2013)
• Engineering Self-Reflection for Human Completion (Coursera, 2017)
• The Nature of Code (Kadenze, 2017)
• Introduction to Programming for the Visual Arts (Kadenze, 2016)
• Java Tutorial for Complete Beginners (Udemy, 2015)

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